Prognosis of prostate cancer with computerized histomorphometric features of tumor morphology from routine hematoxylin and eosin slides

ABSTRACT

Embodiments facilitate generating a biochemical recurrence (BCR) prognosis by accessing a digitized image of a region of tissue demonstrating prostate cancer (CaP) pathology associated with a patient; generating a set of segmented gland lumen by segmenting a plurality of gland lumen represented in the region of tissue using a deep learning segmentation model; generating a set of post-processed segmented gland lumen; extracting a set of quantitative histomorphometry (QH) features from the digitized image based, at least in part, on the set of post-processed segmented gland lumen; generating a feature vector based on the set of QH features; computing a histotyping risk score based on a weighted sum of the feature vector; generating a classification of the patient as BCR high-risk or BCR low-risk based on the histotyping risk score and a risk score threshold; generating a BCR prognosis based on the classification; and displaying the BCR prognosis.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/878,894 filed Jul. 26, 2019, the contents of whichare herein incorporated by reference in their entirety.

FEDERAL FUNDING NOTICE

This invention was made with government support under the grantsCA199374, CA202752, CA208236, CA216579, CA220581, RR012463, CA239055,EB028736, RR12463, awarded by the National Institutes of Health; grantIBX004121A awarded by the United States Department of Veterans Affairs;grants W81XWH-19-1-0668, W81XWH-15-1-0558, W81XWH-18-1-0440, andW81XWH-16-1-0329 awarded by the United States Department of Defense; andgrants CON501692, and DGE1451075 awarded by the National ScienceFoundation. The government has certain rights in the invention.

BACKGROUND

One curative treatment for prostate cancer (CaP) is radicalprostatectomy (RP). RP is the surgical removal of the entire prostate.Following RP, some patients will experience the return of cancer, knownas biochemical recurrence (BCR). BCR may be detected by two consecutiveserum prostate-specific antigen (PSA) test results >0.2 ng/mL. BCR is asurrogate endpoint for CaP, carrying a hazard ratio (HR) of 4.32 fordisease-specific death. Adjuvant therapy reduces the risk of metastasisand disease-specific death, although adjuvant therapy is not appropriatefor all patients due to the low overall mortality rate of CaP. If apatient's future BCR status could be known at the time of RP surgery,high-risk patients could begin adjuvant therapy sooner with the goal ofavoiding metastasis, while low-risk patients could be spared themorbidity associated with further treatment.

Existing BCR prognostic tools, including nomograms, produce a risk scorebased on several clinical variables, but require human observers forGleason grading. Existing approaches thus suffer from intra-observer andinter-observer variations. In addition to nomograms, some existing BCRprognosis approaches employ molecular companion diagnostics for outcomeprognosis. However, these existing approaches are tissue destructive,expensive, and their results are only available after a significantdelay, which may postpone treatment. Furthermore, existing CaPdiagnostic tools are exclusively prognostic, rather than predictive ofbenefit of therapy, and none provide perfect risk stratification. Thus,existing approaches to BCR prognosis or predicting benefit of therapy inCaP are sub-optimal.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate various example operations,apparatus, methods, and other example embodiments of various aspectsdiscussed herein. It will be appreciated that the illustrated elementboundaries (e.g., boxes, groups of boxes, or other shapes) in thefigures represent one example of the boundaries. One of ordinary skillin the art will appreciate that, in some examples, one element can bedesigned as multiple elements or that multiple elements can be designedas one element. In some examples, an element shown as an internalcomponent of another element may be implemented as an external componentand vice versa. Furthermore, elements may not be drawn to scale.

FIG. 1 illustrates a workflow diagram of an example method or set ofoperations according to various embodiments discussed herein.

FIG. 2 illustrates a workflow diagram of an example method or set ofoperations according to various embodiments discussed herein.

FIG. 3 illustrates a workflow diagram of an example method or set ofoperations according to various embodiments discussed herein.

FIG. 4 illustrates a workflow diagram of an example method or set ofoperations according to various embodiments discussed herein.

FIG. 5 illustrates a workflow diagram of an example method or set ofoperations according to various embodiments discussed herein.

FIG. 6 illustrates a workflow diagram of an example method or set ofoperations according to various embodiments discussed herein.

FIG. 7 illustrates a table of cohort data and scanner hardwareassociated with exemplary training cohorts.

FIG. 8 illustrates a table of clinical data associated with exemplarytraining cohorts.

FIG. 9 illustrates a CONSORT diagram of clinical data associated withexemplary training cohorts.

FIG. 10 illustrates an exemplary workflow for histotyping according tovarious embodiments discussed herein.

FIG. 11 illustrates ground truth annotations and segmentation results onheld-out test images.

FIG. 12 illustrates a table of exemplary quantitative histomorphometry(QH) features according to various embodiments discussed herein.

FIG. 13 illustrates Kaplan-Meier BCR-free survival plots of CaP patientsaccording to various embodiments discussed herein.

FIG. 14 illustrates Cox proportional hazard univariable (VA) andmultivariable analysis of BCR on a validation set.

FIG. 15 illustrates Kaplan-Meier BCR-free survival plots of CaP patientsaccording to various embodiments discussed herein.

FIG. 16 illustrates concordance between Decipher and Histotyping riskcategories in patients from different institutions.

FIG. 17 illustrates a diagram of an example apparatus that canfacilitate prognosis of BCR according to various embodiments discussedherein.

FIG. 18 illustrates a diagram of an example apparatus that canfacilitate prognosis of BCR according to various embodiments discussedherein.

FIG. 19 illustrates a diagram of an example computer in whichembodiments described herein may be implemented.

FIG. 20 illustrates a workflow diagram of an example set of operationsaccording to various embodiments discussed herein.

FIG. 21 illustrates an example set of segmented gland lumen.

FIG. 22 illustrates a workflow diagram of an example set of operationsaccording to various embodiments discussed herein.

FIG. 23 illustrates an example risk score threshold value according tovarious embodiments discussed herein.

DETAILED DESCRIPTION

Quantitative histomorphometry (QH) is the automated analysis ofdigitized pathology tissue imagery through feature mining and machinelearning. QH may analyze primitives represented in digitized pathologytissue imagery, including, for example, gland lumens, nuclei, and imagetexture, to calculate features which quantitatively characterize tissuemorphology. These features may then be correlated with disease behaviourthrough machine learning models for diagnosis or outcome prognosis.However, some existing approaches rely on black-box features that do notclearly map to tumor morphology, which limits their clinical adoption.Additionally, there is an absence of validation of existing QHtechniques on large multi-site cohorts, due to the pre-analyticvariability between sites in, for example, sample preparation.

Embodiments include operations, methods, apparatus, and otherembodiments that facilitate rapid, objective risk assessment to identifypatients who are likely to have adverse outcomes after primary treatmentfor CaP. Embodiments facilitate automated assessment of tissue orglandular morphology to generate a prediction of BCR risk following RP.Embodiments may identify specific quantitative measures associated withaggressive CaP to characterize high-risk disease. Embodiments includegeneration of a predictive model robust to sample preparation variation,including sample preparation variation across different institutions.Embodiment may validate the predictive model on an independentvalidation set. Embodiments account for inter-site differences in samplepreparation to facilitate generation of a predictive model thatgeneralizes across imagery associated with patients acquired acrossmultiple, different institutions. Embodiments further add value overexisting risk assessment methods, especially in low-risk patients.Embodiments generate a BCR prognosis using automated analysis of adigitized hematoxylin and eosin (H&E) stained slide of a region oftissue demonstrating CaP associated with a patient. Embodiments maygenerate a BCR prognosis further based on clinical values associatedwith a patient. A prognosis, including a BCR prognosis, may include, forexample, a survival range. Embodiments may optionally further generate ametastasis prognosis.

Some portions of the detailed descriptions that follow are presented interms of algorithms and symbolic representations of operations on databits within a memory. These algorithmic descriptions and representationsare used by those skilled in the art to convey the substance of theirwork to others. An algorithm, here and generally, is conceived to be asequence of operations that produce a result. The operations may includephysical manipulations of physical quantities. Usually, though notnecessarily, the physical quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated in a logic or circuit, and so on.The physical manipulations create a concrete, tangible, useful,real-world result.

It has proven convenient at times, principally for reasons of commonusage, to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, and so on. It should be borne in mind,however, that these and similar terms are to be associated with theappropriate physical quantities and are merely convenient labels appliedto these quantities. Unless specifically stated otherwise, it isappreciated that throughout the description, terms including processing,computing, calculating, determining, and so on, refer to actions andprocesses of a computer system, logic, circuit, processor, or similarelectronic device that manipulates and transforms data represented asphysical (electronic) quantities.

Example methods and operations may be better appreciated with referenceto flow diagrams. While for purposes of simplicity of explanation, theillustrated methodologies are shown and described as a series of blocks,it is to be appreciated that the methodologies are not limited by theorder of the blocks, as some blocks can occur in different orders and/orconcurrently with other blocks from that shown and described. Moreover,less than all the illustrated blocks may be required to implement anexample methodology. Blocks may be combined or separated into multiplecomponents. Furthermore, additional and/or alternative methodologies canemploy additional, not illustrated blocks.

Various embodiments can employ techniques discussed herein to facilitategenerating a prognosis of BCR associated with a patient, or generating aclassification of the patient as low-risk of BCR or as high-risk of BCRin CaP. FIG. 1 illustrates a flow diagram of an example method or set ofoperations 100 that facilitates generating a prognosis associated with apatient demonstrating CaP according to various embodiments discussedherein. The prognosis may be a BCR prognosis. Operations 100 may furtherfacilitate generation of a classification of a patient demonstrating CaPas BCR high-risk or BCR low-risk according to various embodimentsdiscussed herein. A processor(s) may include any combination ofgeneral-purpose processors and dedicated processors (e.g., graphicsprocessors, application processors, etc.). The processors may be coupledwith or may include memory or storage and may be configured to executeinstructions stored in the memory or storage to enable variousapparatus, applications, or operating systems to perform the operationsor methods described herein. The memory or storage devices may includemain memory, disk storage, or any suitable combination thereof. Thememory or storage devices may include, but are not limited to any typeof volatile or non-volatile memory such as dynamic random access memory(DRAM), static random-access memory (SRAM), erasable programmableread-only memory (EPROM), electrically erasable programmable read-onlymemory (EEPROM), Flash memory, or solid-state storage.

Operations 100 includes, at 110, accessing a digitized image of a regionof tissue demonstrating prostate cancer (CaP) pathology. The region oftissue includes a tumor region. The region of tissue may include glandlumen represented in the digitized image. The digitized image includes aplurality of pixels, a pixel having an intensity. The digitized image isassociated with a patient. In one embodiment, the image is a digitizedimage of an H&E stained slide of a region of tissue demonstrating CaP.In one embodiment, the image includes an annotated tumor region. In oneembodiment, the digitized image is acquired at 10× magnification, with aresolution of 1 micron per pixel. In one embodiment, accessing thedigitized image may include downsizing the digitized image. For example,in one embodiment, the digitized image may be downsized to 10×magnification with a resolution of 1 micron per pixel from 20×magnification with a resolution of 0.5 microns per pixel. In oneembodiment, the digitized image may be downsized to 10× magnificationwith a resolution of 1 micron per pixel from 40× magnification with aresolution of 0.25 microns per pixel. The accessed digitized image, forexample, a digitized H&E stained slide, can be stored in memory locallyor remotely, and can be obtained via a medical imaging device one ofconcurrently with method or operations 100 (for example, via a medicalimaging device implementing method or operations 100) or prior to methodor operations 100, or other operations described herein. Accessing thedigitized image, for example, the digitized H&E stained slide image,includes acquiring electronic data, reading from a computer file,receiving a computer file, reading from a computer memory, or othercomputerized activity not practically performed in the human mind.

Operations 100 also includes, at 120, generating a set of segmentedgland lumen by segmenting a plurality of gland lumen represented in thetumor region using a deep learning segmentation model. In oneembodiment, segmenting a gland lumen using a deep learning segmentationmodel includes segmenting a gland lumen using a deep learning modeltrained to segment gland lumen represented in a digitized hematoxylinand eosin (H&E) stained image of a region of tissue demonstrating CaP. Asegmented gland lumen may comprise a boundary. Embodiments may furtherinclude training the deep learning segmentation model according tovarious techniques described herein. In one embodiment, the deeplearning segmentation model is a modified UNet deep learning model.Generating the set of segmented gland lumen includes acquiringelectronic data, reading from a computer file, receiving a computerfile, reading from a computer memory, or other computerized activity notpractically performed in the human mind.

Operations 100 also includes, at 130, generating a set of post-processedsegmented gland lumen by post-processing the set of segmented glandlumen. The presence of artifacts in digitized H&E stained images is aproblem in the segmentation of gland lumen and the extraction offeatures, including QH features, from segmented gland lumen representedin digitized H&E stained images. Embodiments facilitate improvedsegmentation of gland lumen and improved extraction of QH features bypost-processing the set of segmented gland lumen. In one embodiment,generating the post-processed set of segmented gland lumen bypost-processing the set of segmented gland lumen includes defining a setof post-processed segmented gland lumen, where the set of post-processedsegmented gland lumen includes the members of the set of segmented glandlumen. For example, in one embodiment, an initial post-processed set ofsegmented gland lumen may include all the members of the set ofsegmented gland lumen. Embodiments may correct, re-label, or removeobjects incorrectly labelled as segmented gland lumen from the initialpost-processed set of segmented gland lumen according to techniquesdescribed herein. For example, embodiments may detect incorrectlylabelled segmented gland lumen and re-label objects incorrectly labelledas gland lumen. Embodiments may further determine properties associatedwith a member of set of post-processed segmented gland lumen, and removethe member from the set of post-processed segmented gland lumen upondetermining that the member of the set of post-processed segmented glandlumen has, for example, an area less than a threshold area, or hasboundary defined by more than a threshold level of white pixels.Generating the set of post-processed segmented gland lumen includesacquiring electronic data, reading from a computer file, receiving acomputer file, reading from a computer memory, or other computerizedactivity not practically performed in the human mind. FIG. 4 illustratesan example set of operations 400 for generating the set ofpost-processed segmented gland lumen. Operations 400 for generating theset of post-processed segmented gland lumen may be employed byembodiments described herein, including for example, operations 100,200, 300, 600, 2000, 2200, or apparatus 1700 or 1800.

Operations 400 includes, at 410, determining if a member of the set ofpost-processed segmented gland lumen includes a non-lumen region. Forexample, a segmented gland lumen may include an area that has beenincorrectly classified as non-gland lumen by the deep learningsegmentation model. For example, the lumen segmentation model or deeplearning segmentation model trained to segment gland lumen may havecreated a doughnut-shaped segmentation, which is unlikely to be anaccurate representation of a gland lumen since it is unlikely that alumen has non-lumen in the middle surrounded by lumen. FIG. 21illustrates an example set of segmented gland lumen 2110. Example set ofgland lumen 2110 includes segmented gland lumen 2112, 2114, and 2116.Segmented gland lumen 2116 includes an area 2117 that has beenincorrectly classified as non-gland lumen. For example, segmented glandlumen 2116 includes a hole indicated by area 2117.

Returning to FIG. 4, upon determining at 412 that the member of the setof post-processed segmented gland lumen includes a non-lumen region,operations 400 includes, at 414, relabeling the non-lumen region aslumen. FIG. 21 further illustrates an example set of post-processedsegmented gland lumen 2111. Example set of post-processed segmentedgland lumen 2111 is similar to the example set of segmented gland lumen2110, and includes segmented gland lumen 2112, 2114, and 2116. Note thatarea 2117 has been removed from segmented gland lumen 2116 in the set ofpost-processed segmented gland lumen 2111.

Returning to FIG. 4, upon determining at 412 that the member of the setof post-processed segmented gland lumen does not include a non-lumenregion, operations 400 includes, at 420, determining an area of themember of the set of segmented gland lumen. In one embodiment, the areais measured in μm², while in another embodiment, the area may bemeasured in other units, for example, pixels.

Upon determining at 422 that the member of the set of segmented glandlumen has an area less than a threshold area, operations 400 alsoincludes, at 424, removing the member of the set of segmented glandlumen from the set of post-processed segmented gland lumen. In oneembodiment, the threshold area is 4 μm². In another embodiment, thethreshold area may have another, different value, for example, 3 μm², or5 μm², or other value. Removing objects incorrectly labeled as glandlumen, identified by their having an area less than the threshold area,improves the performance of embodiments, including systems, apparatus,or computers in which embodiments are implemented, by excludingnon-lumen objects from analysis of lumen morphology. Excluding non-lumenobjects from analysis of lumen morphology according to techniquesdescribed herein may have the practical effects of reducing computingresources used by systems, apparatus, or computers in which embodimentsare implemented, and improving the accuracy of systems, apparatus, orcomputers in which embodiments are implemented.

Upon determining at 422 that the member of the set of segmented glandlumen has an area greater than or equal to the threshold area,operations 400 includes, at 430, determining a boundary of a member ofthe set of segmented gland lumen. Embodiments may determine the boundaryby dilating the segmented object, for example, the member of the set ofsegmented gland lumen, and subtracting the original object mask from thedilated mask. In this embodiment, the segmented object is dilated by adisk-shaped structuring elements with a radius of 1 pixel. In anotherembodiment, another, different radius may be employed.

Upon determining, at 432, that the boundary of the member of the set ofsegmented gland lumen is defined by a white pixel, operations 400includes, at 434, removing the segmented gland lumen from the set ofpost-processed segmented gland lumen. In this embodiment, a white pixelmay be defined by the pixel's intensity being greater than a threshold.In this embodiment the intensity threshold is a unit-less number havinga value of two-hundred and twenty (220). In this example, the intensitythreshold having a value of two-hundred and twenty (220) is applicablewhere the digitized image is represented in a format in which pixelintensity takes on a value between 0 and 255. In another embodiment, theintensity threshold may have another, different value. In anotherembodiment, the intensity threshold may be represented as a percentageof a maximum threshold, for example, 80%, 86%, or 90%, or as a fractionof a maximum threshold. In one embodiment, determining that the boundaryof the member of the set of segmented gland lumen is defined by a whitepixel includes dilating the boundary of the segmented gland lumen by 1pixel, and determining if the dilated boundary includes more than 5%white pixels. In another embodiment, determining that the boundary ofthe member of the set of segmented gland lumen is defined by a whitepixel includes dilating the boundary of the segmented gland lumen by 1pixel, and determining if the dilated boundary includes more thananother, different percentage of white pixels, for example, 3%, or 7%,or other percentage.

Returning to FIG. 1, operations 100 also includes, at 140, extracting aset of quantitative histomorphometry (QH) features from the digitizedimage based, at least in part, on the set of post-processed segmentedgland lumen. In one embodiment, the members of the set of QH featuresmay be selected based on stability filtering, or Cox regressionmodelling with elastic net regularization, from digitized H&E stainedslides acquired across multiple institutions as described herein. Byselecting the set of QH features based on stability filtering, or Coxregression modelling with elastic net regularization, from digitized H&Estained slides acquired across multiple institutions, embodimentsfacilitate generating a prognosis that accounts for inter-sitedifferences in sample preparation. In another embodiment, the members ofthe set of QH features may be selected using other techniques,including, for example, correlation filtering, Cox regression with LASSOpenalization, Cox regression with ridge penalization, or logisticregression. Extracting the set of QH features includes acquiringelectronic data, reading from a computer file, receiving a computerfile, reading from a computer memory, or other computerized activity notpractically performed in the human mind.

In one embodiment, the set of QH features includes at least ninefeatures. In this embodiment, the set of QH features includes a set ofgland lumen features, a set of sub-graph features, and a set of texturefeatures. In this embodiment, the set of gland lumen features is based,at least in part, on the post-processed set of segmented gland lumen. Inthis embodiment, the set of texture features includes at least onetexture feature extracted from the tumor region. In this embodiment, theset of sub-graph features includes at least one sub-graph featureextracted from the tumor region. In one embodiment, the set of QHfeatures includes at least seven gland lumen shape features, at leastone sub-graph feature, and at least one texture feature. In oneembodiment, the set of QH features includes a mean invariant moment 2feature, a mean Fourier descriptor 4 feature, a standard deviation ofsmoothness feature, a median distance ratio feature, a 5^(th)percentile/95^(th) percentile perimeter ratio feature, a 5^(th)percentile/95^(th) percentile Fourier descriptor 1 feature, a 5^(th)percentile/95^(th) percentile Fourier descriptor 6 feature, a skewnessof edge length sub-graph feature, and a Haralick mean correlationfeature. In another embodiment, another, different number of QH featuresmay be extracted, or the set of QH features may include other, differentQH features.

In one embodiment, extracting the set of QH features includes extracting216 gland lumen features. In this embodiment, 26 Haralick texturefeatures are extracted from the entire tumor region. In anotherembodiment, another, different number of gland lumen features may beextracted, or another, different number of Haralick texture features maybe extracted.

Operations 100 also includes, at 150, generating a feature vector basedon the set of QH features. In one embodiment, a 242 element featurevector is generated based on 216 extracted gland lumen features and 26extracted Haralick features, extracted from the set of post-processedgland lumen and the tumor region represented in the digitized H&Estained image. In another embodiment, the feature vector may includeanother, different number of elements. For example, when the set of QHfeatures includes nine (9) QH features, the feature vector may have nineelements. In another embodiment, generating the feature vector based onthe set of QH features may include generating a feature vector havingthe same cardinality as the set of QH features.

Embodiments may normalize the feature vector by normalizing the set ofQH features that comprise the feature vector. In one embodiment,normalizing the set of QH features includes subtracting the 242 elementvector of the training set feature mean values from the feature vectorsfrom each image, followed by element-wise division of the digitized H&Estained image's feature vector by the 242 element training set featurestandard deviation values. In another embodiment, other normalizationtechniques may be employed.

Operations 100 also includes, at 160, computing a histotyping risk scorebased on a weighted sum of the feature vector. In one embodimentcomputing the histotyping risk score includes multiplying a normalizedfeature vector by a vector of β values. The vector of β values may beobtained from a histotyping model trained according to techniquesdescribed herein. In this embodiment, computing the histotyping riskscore further includes computing the sum of the products of thenormalized feature vectors and their corresponding β values. In oneembodiment, all but nine elements of the β vector, corresponding to thefeatures included in the trained model, are zero. In this example, thenine elements of the β vector, corresponding to the features included inthe trained model correspond to the set of QH features extracted at 140.In one embodiment, the computed histotyping risk score has a valuewithin the range [−0.72, 0.46]. In this embodiment, a higher value, forexample, 0.4 is associated with an increased risk of BCR, while a lowervalue, for example, −0.5, is associated with a lower risk of BCR. Inanother embodiment, the histotyping risk score may have a value withinanother, different range, for example, [−1, 1], or other range. Inanother embodiment, all but another, different number of elements of theβ vector, for example, five or fifteen, corresponding to five or fifteenfeatures included in the trained model respectively, are zero. Inembodiments described herein, the vector of β values can be obtained oneof concurrently with method or operations 100 (for example, via asystem, apparatus, computer, or medical imaging device implementingmethod or operations 100) or prior to method or operations 100, or otheroperations described herein. In another embodiment, the feature vectoris non-normalized, and computing the histotyping risk score includesmultiplying the non-normalized feature vector by the vector of β values.Computing the histotyping risk score includes acquiring electronic data,reading from a computer file, receiving a computer file, reading from acomputer memory, or other computerized activity not practicallyperformed in the human mind.

Operations 100 also includes, at 170, generating a classification of thepatient as BCR high-risk or BCR low-risk based on the histotyping riskscore and a risk score threshold. In one embodiment, generating theclassification includes applying a risk score threshold identified onthe training set to the histotyping risk score computed at 160 tostratify or categorize the patient as BCR low-risk or BCR high-riskaccording to whether the image risk score is less than (<) the riskscore threshold value, or greater than or equal to (>=) the risk scorethreshold value. In one embodiment, the risk score threshold has a valueof 0.0960. In another embodiment, the risk score threshold may haveanother, different value. The risk score threshold may be determinedbased, at least in part, on a range selected from within the range thehistotyping risk score is obtained, for example, [−0.72, 0.46], [−1, 1],or other, different range. The risk score threshold may be computedaccording to various techniques described herein.

For example, in one embodiment, the risk score threshold is computedbased on midpoints between the histotyping risk score of each trainingset patient. In this embodiment, risk score thresholds which yielded agroup smaller than one-third of the training set or a logrankp-value >0.05 are discarded. Next, embodiments may identify the set ofrisk score thresholds which yielded the maximum absolute difference inmedian survival time between the groups. From among identified riskscore thresholds, the risk score threshold with the largest hazard ratiois selected and applied to the training and validation sets to createHistotyping stratifications. Histotyping stratifications may include,for example, BCR low-risk, or BCR high-risk. In another embodiment,histotyping stratifications may include, other, different clinicalendpoints associated with the patient, for example, metastasis high-riskor metastasis low-risk.

FIG. 23 illustrates an example risk score threshold value determinedaccording to various techniques described herein. FIG. 23 illustrates,at 2310, Histotyping hazard ratio 2314 and 95% confidence interval 2316.FIG. 23 also illustrates, at 2320, difference in median survival time2324. FIG. 23 further illustrates, at 2330, the logrank p-value 2334 inthe training set at various stratification thresholds. The risk scorethreshold value used in this example is marked by a vertical line 2312.

In another embodiment, other classification schemes may be employed. Forexample, in one embodiment, the patient may be classified histotypinglow-risk, histotyping high-risk, or histotyping indeterminate risk. Inanother embodiment, the patient may be classified BCR low-risk, BCRhigh-risk, or BCR indeterminate risk. In one embodiment, where thepatient is classified BCR low-risk or BCR high-risk, the classificationis generated with at least: p<0.0001, HR=2.27, 95% confidence interval:1.59-3.26, and concordance index=0.66. In another embodiment, thepatient may be classified as metastasis high-risk or metastasislow-risk. Generating the classification includes acquiring electronicdata, reading from a computer file, receiving a computer file, readingfrom a computer memory, or other computerized activity not practicallyperformed in the human mind.

Operations 100 also includes, at 180, generating a BCR prognosis based,at least in part, on the classification. Generating the BCR prognosismay include, for example, generating a prognosis that the patient islikely to experience BCR, where the patient is classified as BCRhigh-risk, or generating a prognosis that the patient is unlikely toexperience BCR, where the patient is classified as BCR low-risk. In oneembodiment, the BCR prognosis is prognostic of BCR with: p<0.0001,HR=2.27, 95% confidence interval: 1.59-3.26, and concordance index=0.66.In another embodiment, a metastasis prognosis may be generated based onthe classification. For example, embodiments may generate a prognosisthat the patient is likely to experience metastasis, where the patientis classified as metastasis high-risk, or generating a prognosis thatthe patient is unlikely to experience metastasis, where the patient isclassified as metastasis low-risk. Generating the BCR prognosis includesacquiring electronic data, reading from a computer file, receiving acomputer file, reading from a computer memory, or other computerizedactivity not practically performed in the human mind.

Operations 100 further includes, at 190, displaying the BCR prognosis.In one embodiment, the set of operations 100 further includes, at 190,displaying the BCR prognosis and optionally displaying one or more ofthe classification, the digitized image, the H&E stained image, or thehistotyping risk score. Displaying the BCR prognosis, and optionallydisplaying one or more of the classification, the digitized image, theH&E stained image, or the histotyping risk score may include displayingthe BCR prognosis and optionally displaying one or more of theclassification, the digitized image, the H&E stained image, or thehistotyping risk score on a computer monitor, a smartphone display, atablet display, or other displays. Displaying the BCR prognosis, andoptionally displaying one or more of the classification, the digitizedimage, the H&E stained image, or the histotyping risk score can alsoinclude printing the BCR prognosis, and optionally printing one or moreof the classification, the digitized image, the H&E stained image, orthe histotyping risk score. Displaying the BCR prognosis and optionallydisplaying one or more of the classification, the digitized image, theH&E stained image, or the histotyping risk score can also includecontrolling a CaP BCR prediction system, a personalized medicine system,a medical imaging system, a monitor, or other display, to displayoperating parameters or characteristics of a machine learningclassifier, including a deep learning classifier or deep learning model,during at least one of training and testing of the machine learningclassifier, or during clinical operation of the machine learningclassifier. For example, embodiments may display operating parameters ofa machine learning classifier or deep learning model employed inoperations 100, 200, 300, 400, 500, 600, 2000, or 2200, for example, alumen segmentation model or deep learning model trained to segment glandlumen.

By displaying the BCR prognosis and optionally displaying one or more ofthe classification, the digitized image, the H&E stained image, or thehistotyping risk score, example embodiments provide a timely andintuitive way for a human medical practitioner to more accuratelypredict treatment response, to more accurately stratify or classify anROI or the patient associated with the ROI into a treatment responsecategory (e.g., high-risk of BCR, low risk of BCR), or generate aprognosis for the patient associated with the ROI, thus improving onexisting approaches to predicting BCR or generating a prognosis in CaP.By displaying the BCR prognosis and optionally displaying one or more ofthe classification, the H&E stained image, or the histotyping riskscore, example embodiments may further provide a timely and intuitiveway for a human medical practitioner to more accurately identify CaPpatients at high-risk of BCR, and to improve treatment managementaccordingly. Embodiments may further display a metastasis prognosiscomputed according to techniques described herein.

FIG. 2 illustrates a set of operations 200 that is similar to operations100, but that includes additional elements and details. Operations 200includes operations 110-190, but also includes, at 212, annotating thetumor region represented in the image. In one embodiment, the tumorregion may be automatically annotated using, for example, neuralnetworks, QH techniques, or analysis of immunohistochemically stainedconsecutive slices of tissue. In various embodiments described herein,an annotated tumor region can be obtained one of concurrently withmethod or operations 100, 200, 300, 600, 2000, or 2200 (for example, viaa system, apparatus, computer, or medical imaging device implementingmethod or operations 100, 200, 300, 600, 2000, or 2200) or prior tomethod or operations 100, 200, 300, 600, 2000, or 2200, or otheroperations described herein.

FIG. 3 illustrates a set of operations 300 that is similar to operations100 or 200, but that includes additional elements and details. The setof operations 300 includes operations 110-190. Operations 300 furtherincludes, at 380, generating a second BCR prognosis based, at least inpart, on the histotyping risk score. Operations 300 may further include,at 390, displaying the second BCR prognosis. The second BCR prognosismay be, in various embodiments, a continuous histotyping prognosis. Forexample, in one embodiment, where the histotyping risk score associatedwith a first patient has a value within the range [−0.72, 0.46], thesecond BCR prognosis may comprise a prognosis of increased risk of BCRwhere the histotyping risk score has a higher value, for example, 0.4.In this example, for a patient associated with a histotyping risk scorehaving a lower value, for example, −0.5, the second prognosis maycomprise a different prognosis of a lower risk of BCR. Other ranges maybe employed.

The set of operations 300 may further include, at 396, generating apersonalized CaP treatment plan. The personalized CaP treatment plan maybe generated for the patient of whom the digitized H&E image wasacquired based, at least in part, on the classification, the first BCRprognosis, or the second BCR prognosis, and optionally on one or more ofthe histotyping risk score, or the digitized H&E image. Defining orgenerating a personalized CaP treatment plan facilitates delivering aparticular treatment that will be therapeutically active to the patient,while minimizing negative or adverse effects experienced by the patient.For example, the personalized CaP treatment plan may suggest a surgicaltreatment, may define a pharmaceutical agent dosage or schedule and/orother recommendations for CaP management, for a patient, wherein thespecific recommendation can depend on a classification (e.g., high-riskof BCR) or prognosis associated with the patient. Generating thepersonalized CaP treatment plan includes acquiring electronic data,reading from a computer file, receiving a computer file, reading from acomputer memory, or other computerized activity not practicallyperformed in the human mind.

The set of operations 300 can further include, at 398, optionallydisplaying the personalized CaP treatment plan according to embodimentsdescribed herein.

Embodiments may train a deep learning model to segment gland lumenrepresented in digitized H&E stained imagery of tissue demonstratingCaP. FIG. 5 illustrates one example set of operations 500 for training adeep learning model to segment gland lumen represented in digitized H&Estained imagery of tissue demonstrating CaP. Operations 500 include, at510, selecting a plurality of regions of interest (ROIs) from a set ofdigitized H&E stained images of tissue demonstrating CaP. In oneembodiment, twenty nine (29) 2000×2000 pixel ROIs at 0.5microns-per-pixel (20× magnification) are selected from the tumorregions of twenty nine (29) slides of a training set of digitized H&Estained images. ROIs may be chosen to represent a range of tissuemorphology, for example cancers of various cancer grades, tissuepreparation artifacts, areas of varying glandular density, areas ofglandular atrophy, and areas of varying lymphocytic infiltration.Members of the set of digitized H&E stained images may be acquiredacross different institutions, or may have been acquired using differentacquisition parameters.

Operations 500 also includes, at 520, annotating a gland lumenrepresented in a member of the plurality of ROIs. In one embodiment,gland lumen may be annotated using machine learning or deep learningtechniques. A gland lumen includes a lumen boundary. In anotherembodiment, annotations may be performed by an expert human pathologist.In one embodiment, annotations may be performed using QuPath v0.12. Inone embodiment, annotations may be cleaned to increase fidelity to thelumen boundary. In another embodiment, a set of digitized H&E stainedimages may be accessed that has gland lumen annotated prior to theexecution of operations 500. For example, a set of digitized H&E stainedimages may be accessed that has gland lumen already annotated can beobtained via a computer, apparatus, system, or medical imaging deviceone of concurrently with method or operations 500 (for example, via amedical imaging device implementing method or operations 500) or priorto method or operations 500, or other operations described herein.

Operations 500 also includes, at 530, generating a first set of resizedROIs by resizing a member of the plurality of ROIs. In one embodiment,generating the set of resized ROIs comprises resizing a member of theplurality of ROIs to one μm per pixel. In another embodiment, an ROI maybe resized to other sizes, including, for example, 2 microns-per-pixel.By reducing the number of pixels in the image, embodiments facilitatefaster training of the deep learning model and reduce the time needed toapply the model to a new image, thereby improving the performance of theapparatus, system, or computer implementing embodiments describedherein. In one embodiment, generating the first set of resized ROIsincludes resizing all the members of the plurality of ROIs. In anotherembodiment, generating the first set of resized ROIs includes resizing athreshold number of the members of the plurality of ROIs, for example,75% or 90% of the members of the plurality of ROIs. Operations 500 alsoincludes, at 540, training a first deep learning model to segment agland lumen based on the first set of resized ROIs. In one embodiment,the first deep learning model is trained using the first set of resizedROIs. In this embodiment, the first deep learning model is a modifiedUNet deep-learning model, and twenty nine (29) ROIs as selected at step510 are employed.

Operations 500 also includes, at 550, evaluating the first deep learningmodel performance. In one embodiment, evaluating the first deep learningmodel performance includes selecting and annotating a set of additionalROIs which display morphology similar to that on which the deep learningmodel performed poorly. In one embodiment, identification ofmorphologically similar images comprises qualitatively identifyingattributes of the ROI, such as tumor grade, glandular appearance, andcell-type frequency, and locating visually similar ROIs not yet includedin the model training set. In one example, poor performance is definedas a substantially lower per-pixel accuracy than other images in the setof ROIs, or poor performance may be determined by a qualitativeexamination revealing large areas of incorrect segmentation. In oneembodiment, the deep learning model is evaluated using a set of anadditional twelve (12) 2000 pixel×2000 pixel ROIs which displayedmorphology similar to that on which the first deep learning modelperformed poorly.

Operations 500 also includes, at 560, training a final deep learningmodel on a second training set, where the second training set includesthe first set of resized ROIs, and a second, different set of annotatedresized ROIs, where the second, different set of annotated resized ROIsis selected based on the evaluated first deep learning modelperformance. In one embodiment, generating the second different set ofannotated resized ROIs includes resizing a member of the plurality ofROIs to one μm per pixel. In one embodiment, the final deep learningmodel is trained on a set of 41 annotated ROIs, while in anotherembodiment, another, different number of annotated ROIs may be employed.

Operations 500 further includes, at 570, testing the final deep learningmodel. Testing the final deep learning model may include testing thefinal deep learning model on a set of held-out testing images. In oneembodiment, four ROIs, for example ROIs 1110, 1120, 1130, and 1140,illustrated in FIG. 11, are used for testing the deep learning model. Inone embodiment, FIG. 11 illustrates ground truth annotations 1151(green) and segmentation results 1153 (blue) for four held-out testimages 1110, 1120, 1130, and 1140. In this embodiment, the final deeplearning model achieved a pixel-wise true positive rate of 0.97 and atrue negative rate of 0.97 on these four held-out test images 1110,1120, 1130, and 1140. In one embodiment, the final deep learning modelis the model in the epoch having the minimum loss function value on thefour held out images 1110, 1120, 1130, and 1140. In this embodiment, thefinal deep learning model had a depth of four (4) with a total of1,928,450 parameters. In one embodiment, the optimal deep learning modelwas reached after 290 epochs, which was reached after 103.5 minutesusing an Nvidia Titan Xp GPU.

Embodiments may compute a CaP BCR prognosis based on the histotypingrisk score and further based on clinical factors associated with thepatient. Embodiments may compute a CaP BCR prognosis based on ahistotyping-plus risk score, where the histotyping-plus risk score iscomputed based on the histotyping risk score and additionally onclinical factors associated with the patient. Clinical factorsassociated with the patient may include, for example, a pre-radicalprostatectomy (RP) serum prostate specific antigen (PSA) level, or aGleason grade group. In another embodiment, a clinical factor(s)associated with the patient may include another, different clinicalfactor(s), including, for example, surgical margin positivity, number ofpositive lymph nodes, patient age, or seminal vesicle invasion status.FIG. 6 illustrates an example set of operations 600. Operations 600 aresimilar to operations 100 but include additional steps. Operations 600includes operations 110-190, and further includes, at 610 accessing apre-RP serum PSA level value associated with the patient. For example,the patient may have, prior to implementation of operations 600,undergone pre-operative, for example, pre-radical prostatectomy, serumprostate specific antigen testing. In one embodiment, the PSA levelvalue may be defined in ng/mL.

Operations 600 also includes, at 620, accessing a Gleason grade groupvalue associated with the patient. For example, in one embodiment, aGleason grade group value may be computed one of concurrently withmethod or operations 600 (for example, via a medical imaging deviceimplementing method or operations 600) or prior to method or operations600, or other operations described herein, based on the digitized image.In one embodiment, the Gleason grade group value may have a value of 1,2, 3, 4, or 5.

Operations 600 also includes, at 630, computing a histotyping-plus riskscore. The histotyping-plus risk score may be computed as a function ofthe histotyping risk score, the PSA level value, and the Gleason gradegroup value. In one embodiment, computing the histotyping-plus riskscore includes generating a second feature vector, where the secondfeature vector includes the value of the histotyping risk score, thepre-operative serum PSA level in ng/mL, and four binary variablescorresponding to whether the patient was assigned Gleason grade group 2,3, 4, or 5, respectively. The second feature vector is multiplied by theβ values from the trained Histotyping-plus model according to techniquesdescribed herein. In one embodiment, the nonzero elements of the βvector correspond to Histotyping score, pre-operative PSA level, Gleasongrade group 3, and Gleason grade group 4. The sum of the products of thefeature vector and β values is the Histotyping-plus risk score. Inanother embodiment, the nonzero elements of the β vector may correspondto other, different Gleason grade groups, or other different clinicalfactors, including, for example, surgical margin positivity, number ofpositive lymph nodes, patient age, or seminal vesicle invasion status.

Operations 600 also includes, at 640, generating a second classificationof the patient as BCR high-risk or BCR low-risk based on thehistotyping-plus risk score, and a histotyping-plus risk scorethreshold. In one embodiment, the histotyping-plus risk score thresholdhas a value of 1.331. In another embodiment, the histotyping-plus riskscore threshold may have another, different value, including, forexample, 1.1 or 1.5, or other value.

In one embodiment, generating the second classification includesapplying a histotyping-plus risk score threshold identified on thetraining set to the histotyping-plus risk score computed at 630 tostratify or categorize the patient as BCR low-risk or BCR high-riskaccording to whether the histotyping-plus risk score is less than (<)the histotyping-plus risk score threshold value, or greater than orequal to (>=) the histotyping-plus risk score threshold value. In oneembodiment, the histotyping-plus risk score threshold has a value of1.331. In another embodiment, the histotyping-plus risk score thresholdmay have another, different value. The histotyping-plus risk scorethreshold may be determined based, at least in part, on a range selectedfrom within the range the histotyping-plus risk score is obtained, forexample, [−1.210, 2.510], [−1.5, 3], [−0.5, 0.5], [−1, 1], or otherrange. The histotyping-plus risk score threshold may be computedaccording to various techniques described herein. For example, thehistotyping-plus risk score threshold may, in one embodiment, bedetermined as the threshold that maximizes the hazard ratio in BCR-freesurvival time between histotyping-plus low-risk and histotyping-plushigh-risk patients.

Operations 600 also includes, at 650, generating a histotyping-plus BCRprognosis based, at least in part, on the second classification. Forexample, generating the histotyping-plus BCR prognosis may include, forexample, generating a prognosis that the patient is likely to experienceBCR, where the patient is classified as BCR high-risk, or generating aprognosis that the patient is unlikely to experience BCR, where thepatient is classified as BCR low-risk. In one embodiment, thehistotyping-plus BCR prognosis is prognostic of BCR in CaP with:p<0.001, HR=3.23, 95%, CI: 1.68-6.21, and concordance index=0.75.

Operations 600 further includes, at 660, displaying the histotyping-plusBCR prognosis according to various techniques described herein.

Techniques and aspects of various embodiments are further explainedbelow, in connection with an example embodiment that facilitates, for apatient demonstrating CaP, generating a BCR prognosis associated withthe patient, or classifying the patient as a BCR high-risk patient or aBCR low-risk patient based on pathology imagery, including digitized H&Eimagery, associated with the patient.

Example Use Case: Computerized Histomorphometric Features of TumorMorphology from Routine Hematoxylin and Eosin Slides are Prognostic inProstate Cancer

An example embodiment included training a machine learning model todistinguish CaP patients with a low-risk of BCR from CaP patients with ahigh-risk of BCR, based on example H&E stained imagery of tissuedemonstrating CaP, and quantitative morphology features extracted fromthe digitized H&E stained imagery. In this example, a study populationconsisted of n=896 patients from six sources: the University ofPennsylvania (UPenn), University Hospitals Cleveland Medical Center(UH), New York Presbyterian Hospital/Weill Cornell Medical Center (WCM),the University of Turku (UTurku), the Cancer Genome Atlas (TCGA), andIcahn School of Medicine at Mount Sinai (MS). Patient images weredigitized on a variety of whole slide scanners, described in table 700illustrated in FIG. 7. TCGA patients previously identified as havingdiscrepancies in outcome information were excluded.

In this example, patients were divided into a training set and avalidation set. The training set was composed of n=70 UPenn patients andn=145 UH patients. Within the UPenn cohort were two sub-cohorts, with 35patients each. Each sub-cohort was collected a different time. Thevalidation set consisted of the remaining n=681 patients from five sites(UPenn, WCM, UTurku, TCGA, MS). The UPenn patients were split betweentraining and validation sets based on the scanner used to digitized theslides. The training set was selected to include approximately a quarterof the overall study dataset while containing patients from multipleinstitutions to enable analysis of feature stability across staining andscanning differences.

In this example, the study cohorts are summarized in table 800 of FIG.8, and FIG. 9. Table 800 illustrated in FIG. 8 illustrates clinical datafor the 896 patients included in the study in this example. FIG. 9illustrates a CONSORT diagram 900 of study cohorts in this example.Patients were included in this study if they had both a successfullydigitized diagnostic slide and post-RP PSA test results available for nofewer than 30 days post-surgery. Patients who received neo-adjuvant oradjuvant therapy were excluded from the study. Patients were consideredto have experienced BCR after two consecutive PSA serum tests >0.2ng/mL. Non-BCR patients were censored at the date of last available PSAtest.

In this example, a subset of the validation set consisting of 144 UPennand 29 MS patients had Decipher genomic classifier results available andwere used to compare embodiments employing Histotyping as describedherein to Decipher. This cohort of UPenn patients consisted of allpatients consenting to research who were operated on by the same singlesurgeon, respectively, before Jul. 1, 2017 who had PSA follow-upinformation and Decipher score results available.

In this example, the highest grade slide, for UTurku patients, ordiagnostic slide, for all other sources, of each patient was digitizedin a whole-slide scanner. In this example, for all cases, the slide andtumor nodule used were determined by a genitourinary pathologist. Asingle representative cancerous region, selected to include the highestgrade cancer on the slide, was annotated on each digital image. Trainingset images also had a representative non-cancerous region annotated forthe feature stability filtering step of model training. In embodimentsdescribed herein, automated techniques, including deep learningtechniques, may be employed to annotate a cancerous or tumoral region,or a representative non-cancerous region.

FIG. 10 illustrates an example workflow according to embodimentsdescribed herein. FIG. 10 illustrates, at 1010, annotation of arepresentative tumor region on a whole-slide image. FIG. 10 alsoillustrates, at 1020, results of automated gland segmentation 1021 andfeature visualizations from a region of interest in the annotatedlesion. Illustrated at 1023 is a Voronoi diagram, constructed by edgeswhich are equidistant from adjacent glands. Illustrated at 1025 is thefirst invariant moment, which is equivalent to moment of inertia.Further illustrated is the distance ratio at 1027, which is the ratio ofa gland's average radius to its maximum radius. For clarity ofillustration, in the first invariant moment illustrated at 1025 anddistance ratio illustrated at 1027, glands are shaded according to theirfeature values.

FIG. 10 also illustrates, at 1030, steps of model training of aclassifier for generating a prognosis of BCR in CaP, where featuresextracted from segmented gland lumen are filtered for stability at 1032using the three cohorts of the training set. A Cox regression model isthen fitted using 10-fold elastic-net regularization, illustrated at1036. In one example, model training may additionally includecorrelation filtering, illustrated at 1034.

FIG. 10 further illustrates, at 1040, results of model training. In thisexample, each patient of the n=263 training set is represented as a dotin the scatter plot, with patients who had BCR as red dots, for exampledots 1041 and 1042, and censored patients as blue dots, for example dots1043 and 1044. Dots are located on the x-axis (horizontal axis) at theirtime of BCR or time of last PSA test and on the y-axis (vertical axis)at their Histotyping risk score from the Cox regression model. Thestratification threshold or histotyping risk score threshold identifiedon the training set is shown as a horizontal black line 1045 and is usedto classify patients as BCR low-risk or BCR high-risk. Regions ofinterest from patients at various risk scores are shown in boxes 1046,1047, 1048 and 1049.

In this example, gland lumen segmentation was performed by a deeplearning model. In one example, a modified UNet architecture may beemployed, while in another example, other deep learning modelarchitectures may be employed. In this example, segmentation of glandlumen represented in the imagery was performed by a modifiedUNet-inspired deep learning model according to various techniquesdescribed herein. In this example, the deep learning segmentation modelwas trained on 41 1000×1000 pixel regions cropped from 37 trainingslides annotated for gland lumen. On the four regions held out fortesting, illustrated in FIG. 11 at 1110, 1120, 1130, and 1140, the modelyielded a per-pixel true positive rate and true negative rate of 0.97.Images were resized to 1 MPP (equivalent to 10× magnification) for lumensegmentation. In this example, the model was then applied to all 896images, and the segmentation results were visually verified to besuitable for feature extraction. In another example, the regions croppedfrom the training slides may have other, different dimensions, forexample, 2000×2000 pixels. Other magnification levels, including, forexample, 20× (0.5 microns per pixel) or 5× (2 microns per pixel), may beemployed by embodiments described herein.

In this example, a total of 242 features were extracted from the largesttumor region on each patient slide, of which a subset of nine (9)features were used in Histotyping. 216 of these features weredescriptors of gland morphology and architecture and were extracted fromthe gland segmentations. 26 Haralick texture features were extractedfrom the entire annotated tissue region, with no regard for thesegmentations. In this example, these features were selected based ontheir past performance in prostate cancer grading and BCR prognosis.FIG. 12 illustrates a table 1200 of the subset of nine (9) QH featuresused in this example. In this example, the subset of nine (9) QHfeatures includes a mean invariant moment 2 shape feature, a meanFourier descriptor 4 shape feature, a standard deviation of smoothnessshape feature, a median distance ratio shape feature, a 5^(th)percentile/95^(th) percentile perimeter ratio feature, a 5^(th)percentile/95^(th) percentile Fourier descriptor 1 feature, a 5^(th)percentile/95^(th) percentile Fourier descriptor 6 feature, a skewnessof edge length sub-graph feature, and a Haralick mean correlationfeature.

Accounting for differences in slide preparation and artifacts caused bysaid differences is a problem in CaP BCR prognosis. For example, sincepatients, including patients in the study dataset, may originate frommany institutions, there may be variability in specimen preservation,fixation, sectioning, and staining as well as slide digitizationhardware based on the protocols and equipment at each institution. Thesesources of pre-analytic variation affect the final appearance of theslide images and could therefore affect the features extracted fromthese images.

Embodiments provide a solution or solutions to this problem ofdifferences in slide preparation and artifacts caused by saiddifferences. For example, in this example, features highly susceptibleto site-specific factors were removed to improve model performance. Thisfiltering was performed using the two sub-cohorts of the UPenn patientsseparately, as there were qualitative visual differences between thesub-cohorts, and all the UH patients. This analysis was restricted tothe non-cancerous regions to eliminate the confounding effect of tumormorphology on stability calculations. These restrictions caused thethree sub-cohorts used in this step to contain 36, 37, and 93 patients.In this example, three quarters of the patients in each sub-cohort wererandomly selected and features were evaluated with the Wilcoxon rank sumtest for a significant difference between each pairwise combination ofcohorts. This random sub-sampling and comparison was repeated 1000times. Features significantly different in more than 10% of theseiterations were discarded.

In this example, features which passed stability filtering at 1032 wereused to train a Cox regression model via 10-fold elastic-netregularization (α=0.5) at 1036. Features were normalized using thetraining set to have a mean of 0 and standard deviation of 1 so thathazard ratios would be comparable across features. The β values of thefinal model, containing 9 features, for example, the nine (9) QHfeatures listed in table 1200, were then applied to the training andvalidation sets to obtain a risk score for each patient. In thisexample, nine (9) features were included in the model as that was thenumber of features which minimized the deviance in 10-fold crossvalidation during training. The output of this step was a histotypingrisk score for each patient which could take on any value and wasunbounded, though for all patients in this study in this example rangedfrom −0.76 to 0.42. Other ranges may be employed.

Embodiments may determine or employ a risk score threshold. In thisexample, to find the optimal risk score threshold for stratifyinglow-risk and high-risk patients, each midpoint of the risk scores ofconsecutive training set patients was considered. First, thresholdswhich yielded a group smaller than one-third of the training set or alogrank p-value >0.05 were discarded. Next, the set of thresholds whichyielded the maximum absolute difference in median survival time betweenthe groups was identified. From among identified thresholds, theidentified threshold with the largest hazard ratio was selected andapplied to the training and validation sets to create the Histotypingrisk groups. In one embodiment, the risk score threshold value may be,for example, 0.0960. Other risk score threshold values may be employed,for example, 0.08, 0.1, or other value.

Embodiments may evaluate the performance of methods, operations,apparatus, or other embodiments for generating a CaP BCR prognosis. Inthis example, the performance of Histotyping was evaluated in thevalidation set using the separation in BCR-free survival time betweenthe low-risk and high-risk groups by logrank p-value, by hazard ratio,and by concordance index (c-index). Model independence was evaluated ina Cox proportional hazards with Histotyping risk score, Gleason gradegroup, margin positivity, pathological tumor stage, and preoperativePSA. To further validate the added value of Histotyping, clinicallylow-risk and high-risk cohorts were analyzed separately to determine ifHistotyping according to embodiments described herein added value on topof clinical stratifications. Histotyping results in two clinicallystratified cohorts (Gleason grade group 3, margin negative) arediscussed here.

In this example, the performance of embodiments described herein, (e.g.,Histotyping) was compared to Decipher for BCR prognosis in the 173patients of the validation set who had Decipher score information.Decipher scores were calculated based on the predefined 22-markerDecipher classifier. The Decipher score is a continuous score between 0and 1, with the lowest scores indicating a lower risk of metastasis.Patients with high score (>0.6) were categorized as high risk, patientswith 0.45-0.6 as average risk, and patients with <0.45 as low risk.

In this example, additionally, a second elastic-net penalized Coxregression model was constructed on the training set using Histotyping,preoperative PSA level, and Gleason grade group to create theHistotyping-plus model. These covariates were chosen for this experimentas they were available in n=148 training set patients, more than for anyother pair of clinical covariates. The Histotyping-plus model was thenvalidated on all n=173 patients of the Decipher validation set andcompared to Decipher by c-index. For stratification into low-/high-riskgroups, for example, BCR low-risk/BCR high-risk, a new decisionthreshold was chosen using the training set in the same process as forHistotyping. In one embodiment, the new histotyping-plus risk scorethreshold value is 1.331, while in another embodiment, other thresholdvalues may be employed.

For example, in one embodiment, a pre-operative prostate specificantigen (PSA) value is obtained for a patient. In this example, aGleason grade group associated with the patient is obtained. In thisexample, a continuous histotyping risk score is computed according totechniques described herein. In this example, then a Histotyping-plusfeature vector is generated, where the Histotyping-plus feature vectorincludes the Histotyping risk score, the pre-operative serum PSA levelin ng/mL, and four binary variables corresponding to whether the patientwas assigned Gleason grade group 2, 3, 4, or 5, is multiplied by the βvalues from a trained Histotyping-plus model. The nonzero elements ofthe β vector correspond to Histotyping score, pre-operative PSA level,Gleason grade group 3, and Gleason grade group 4. Embodiments maycompute the sum of the products of the feature vector and β values todetermine the Histotyping-plus risk score. In this example, theHistotyping-plus risk score threshold identified on the training set isapplied to the computed Histotyping-plus risk score to classify thepatient as BCR low-risk or BCR high-risk. A prognosis may then begenerated based, at least in part, on the classification.

In this example, Histotyping was significantly prognostic of BCR in thetraining (p<0.001, HR=2.38, 95% CI: 1.37-4.15, c-index=0.63) andvalidation (p<0.001, HR=2.27, 95% CI: 1.59-3.26, c-index=0.66) sets. Thenine (9) features selected by the Cox regression model on the trainingset are shown in Table 1200 in FIG. 12. Seven of nine (9) selectedfeatures were lumen shape features. The prevalence of shape features inthe model is in part a product of the stability filtering. Althoughshape features accounted for 100 of the 242 (41%) features explored inthis study, they were 37 of the 47 (79%) features which passed thestability filtering step.

In this example, embodiments employing Histotyping according totechniques described herein facilitate providing added value in at leastpatients with (a) Gleason grade group 3 (HR=3.48) and (b) negativesurgical margins (HR=2.58). The BCR-free survival time of each riskcategory in the training set is shown in FIG. 13. In this example,Histotyping was significantly prognostic both as a continuous score(p<0.001, HR=1.33, 95% CI: 1.14-1.54) and as a categorical low/high-riskgrouping (p<0.001, HR=2.38, 95% CI: 1.69-3.34) in a Cox proportionalhazards model independent of Gleason grade group, margin status,pathological tumor stage, and preoperative PSA in the n=648 validationset patients with these clinical variables available, shown in Table1400 in FIG. 14.

FIG. 13 illustrates Kaplan-Meier BCR-free survival plots 1310, 1320,1330, and 1340 of patients categorized as Histotyping low-risk (1312,1322, 1332, 1342) and Histotyping high-risk (1314, 1324, 1334, 1344) inthe n=215 patient training set at 1310, the n=681 patient validation setat 1320, the n=142 patients of the validation set with Gleason gradegroup of 3 at 1330, and the n=411 patients of the validation set withnegative surgical margins at 1340.

FIG. 14 includes table 1400, which illustrates Cox proportional hazardunivariable (UVA) and multivariable (MVA) analysis of BCR including theHistotyping risk score with Gleason grade group, margin status,preoperative PSA, and pathological tumor stage in n=648 patients of thevalidation set.

FIG. 15 illustrates Kaplan-Meier BCR-free survival plots 1510, 1520,1530, and 1540 of the 173 patients of the validation set who hadDecipher score information. Patients are stratified by Histotyping at1510, with low-risk plot 1512 and high risk plot 1514. Patients arestratified by Decipher risk groups at 1520, with low-risk plot 1522,intermediate-risk plot 1523, and high-risk plot 1524. Patients arestratified by Histotyping-plus, which incorporates Histotyping,pre-operative PSA, and Gleason grade group as described herein, at 1530,with low-risk plot 1532 and high risk plot 1534. Patients are stratifiedby Decipher risk groups at 1540, with low and intermediate risk as asingle category plot 1542, and high-risk plot 1544.

In this example, for the n=173 patients who had Decipher scoreinformation, there was not a significant difference in BCR-free survivalbetween Decipher low-risk and intermediate-risk patients (p=0.14). Basedon this, for comparing Decipher to Histotyping categorically, Decipherlow-risk and intermediate-risk patients were grouped together.Histotyping was prognostic in these patients (p=0.02, HR=2.05, 95% CI:1.03-4.07, c-index=0.66) with performance comparable to Decipher(p<0.001, HR=2.76, 95% CI: 1.39-5.48, c-index=0.70).

In this example, embodiments generating a prognosis based on aHistotyping-plus risk score as described herein surpassed Histotypingalone and Decipher alone (p<0.001, HR=3.23, 95% CI: 1.68-6.21,c-index=0.75) using four covariates: Histotyping, preoperative PSA,pathological Gleason grade group 3 (relative to 1), and pathologicalGleason grade group 4 (relative to 1).

In this example, embodiments employing Histotyping are prognostic ofpost-RP BCR-free survival, including in the validation cohort,independent of Gleason grade group, pre-operative prostate-specificantigen density, pathological tumor stage, and surgical marginpositivity. Embodiments employing Histotyping according to techniquesdescribed herein, having a hazard ratio (HR) of 2.27 on the validationset is similar to that of current gold-standard BCR prognosis nomograms(HR=1.09-2.74). Furthermore, Histotyping-plus, incorporating Histotypingalongside Gleason grade group and preoperative PSA, had a higherconcordance index than Histotyping alone and Decipher. Embodimentsemploying Histotyping according to techniques described herein addedvalue in two clinically stratified cohorts which would be categorized aslow-risk or intermediate-risk by existing methods: patients with Gleasongrade group 3 and those with negative surgical margins. Embodimentsemploying Histotyping according to techniques described hereinfacilitate identifying patients who may benefit from adjuvant therapybut would not be likely to be recommended for additional therapy undercurrent BCR prognosis schemes. Embodiments employing Histotypingaccording to techniques described herein facilitate identifyinghigh-risk patients with low-risk clinical markers or intermediate-riskclinical markers due to the lower risk associated with additionaladjuvant therapy versus de-intensifying therapy for clinically high riskpatients.

Accurate post-surgery BCR prognosis, including, for example, post RP BCRprognosis, is a problem in treating CaP. Accurate post-surgery BCRprognosis has substantial implications for patient care and healthcareutilization. While the STAMPEDE trial has demonstrated that adjuvanttherapy can improve patient survival after surgery, not every patientwill benefit from further treatment. Current statistics suggest that tenhigh-risk CaP patients need to receive adjuvant therapy to avoid onedeath, indicating that current BCR prognosis tools are sub-optimal. Thegold standard of these tools, nomograms, are driven by Gleason grading,which is limited by the power of human perception and has only moderateinter-reviewer agreement. Accordingly, there has been an increasingawareness of the need for an objective and accurate BCR prognosis tool.Companion diagnostic assays, such as the Decipher genomic test, havebeen validated for metastasis prognosis. These assays are tissuedestructive, prohibiting retesting, and destroy irreplaceable humantissue. Though they may be prognostic, the long time required to obtaingenetic testing results delays the start of adjuvant therapy.Additionally, molecular testing protocols are expensive andsophisticated, limiting their availability. For example, OncoType DXProstate costs $4520, but still produces a net cost savings of over$2000 per patient by reducing the number of patients receivingtreatment. Thus, embodiments described herein for generating a BCRprognosis may yield a large cost savings in addition to improved patientoutcomes. Embodiments may further facilitate improved performance of BCRprognosis systems, apparatus, or processors, circuits, logics, orcomputers in which embodiments described herein may be implemented orpractically integrated.

Embodiments described herein facilitate a QH-based assay, termedHistotyping, for CaP risk stratification or BCR prognosis generation.The Histotyping risk score according to embodiments described herein issignificantly prognostic of BCR-free survival in the BCR validationcohort independent of Gleason grade group, pre-operativeprostate-specific antigen density, pathological tumor stage, andsurgical margin positivity. Histotyping according to embodimentsdescribed herein added value in two cohorts which would be categorizedas low-risk by current methods: patients with Gleason grade group 2 andthose with negative surgical margin. Histotyping according toembodiments described herein facilitates identification of patients whomay benefit from adjuvant therapy but would not be likely to berecommended for additional therapy under current, existing BCR prognosisschemes. Identifying high-risk patients with low-risk clinical markersis a problem in CaP risk stratification. Embodiments facilitateproviding improved outcomes in CaP due at least to improvedidentification of high-risk patients with low-risk clinical markers due,at least in part, to the lower morbidity risk associated withrecommending additional adjuvant therapy relative to the risks ofde-intensifying therapy for clinically high-risk patients. AmericanSociety for Radiation Oncology/American Urological Association(ASTRO/AUA) guidelines specifically recommend that adjuvant therapy bediscussed with margin positive patients due to the lack of evidence thatmargin negative patients benefit from additional post-RP treatment.Histotyping according to embodiments described herein, has an HR of 2.27on the validation set, and is thus comparable with studies of theperformance of current gold-standard BCR prognosis nomograms onindependent sets (HR=1.09-2.74). The concordance index of embodimentsdescribed herein (CI=0.66) is similar to the performance of the Kattannomogram (CI=0.68). Embodiments thus provide a measurable improvementover existing methods, systems, apparatus, or other devices orapproaches in reliably and accurately predicting patient outcome andimproving treatment management in CaP.

In this example for the n=66 patients of the Decipher validation set,Histotyping according to embodiments described herein showed a strongconcordance with Decipher. To investigate the concordance of Histotypingrisk categories with Decipher risk categories, a set of N=66 patientsfrom the Cleveland Clinic were analyzed alongside the patients of thevalidation set who had Decipher score information. BCR outcomeinformation was not available for Cleveland Clinic patients, which iswhy those patients were not included in the validation set. Thedistribution of Histotyping risk scores for patients in each Decipherrisk Category are shown in FIG. 16. Concordance between Decipher andHistotyping risk categories is illustrated in patients from Universityof Pennsylvania at 1610, Mount Sinai at 1620, and the Cleveland Clinicat 1630.

Embodiments provide for greater stability than existing approaches.Instability across imagery acquired from different institutions is aproblem in CaP BCR prognosis generation. Some gland shape features areuseful for cancer detection, grading, and BCR prognosis. However, whilesome existing approaches have found gland orientation and texturefeatures to be useful in CaP risk assessment, those features did notappear in the top features of embodiments described herein. Histotypingaccording to embodiments described herein does not include thesefeatures partly due to their instability, with just a quarter of texturefeatures and an eighth of orientation features passing the stabilityfiltering step. Existing approaches which rely on unstable features mayhave worse results when using independent validation sets. Furtherdistinguishing embodiments described herein from existing approaches, aBCR prognosis generated according to techniques described herein, forexample, Histotyping, may be associated with Decipher test results, avalidation not used in existing approaches.

In contrast to existing companion diagnostics approaches, embodimentsdescribed herein may require only a routinely acquired diagnostic H&Eslide, a whole-slide scanner capable of scanning at a resolution of 1MPP, and a moderately powerful desktop computer. Once the slide iscreated, Histotyping analysis can be completed in less than twelve (12)hours at a per-unit cost of nearly zero. Unlike other existing digitalpathology approaches for risk assessment that use special stains or deeplearning models with limited interpretability, the Histotypingassessment, including for example, classification of the patient as BCRlow-risk or BCR high-risk, or generation of a BCR prognosis, accordingto embodiments described herein is directly driven by explainabledescriptors, for example, the set of QH features, of tissue morphologyfrom an H&E slide. Embodiments facilitate use of automated morphologicalanalysis techniques as a companion diagnostic to augment existingmethods and as a stand-in for areas where pathological expertise andexpensive molecular tests are not available. Embodiments thus provide asolution to at least the problem of accurate post-surgery BCR prognosis,through the use of automated techniques for stratifying patients by BCRrisk using a single H&E slide, and can identify a high-risk cohort amongpatients who would otherwise be classified as low-risk by existingapproaches. Embodiments that generate a histotyping-plus-based BCRprognosis further based on routine PSA level tests or Gleason scoringfacilitate a similarly improved solution to at least the problem ofaccurate post-surgery BCR prognosis.

In various example embodiments, method(s) discussed herein can beimplemented as computer executable instructions. Thus, in variousembodiments, a computer-readable storage device can store computerexecutable instructions that, when executed by a machine (e.g.,computer, processor), cause the machine to perform methods or operationsdescribed or claimed herein including operation(s) described inconnection with methods or operations 100, 200, 300, 400, 500, 600,2000, or 2200, or any other methods or operations described herein.While executable instructions associated with the listed methods oroperations are described as being stored on a computer-readable storagedevice, it is to be appreciated that executable instructions associatedwith other example methods or operations described or claimed herein canalso be stored on a computer-readable storage device. In differentembodiments, the example methods or operations described herein can betriggered in different ways. In one embodiment, a method or operationcan be triggered manually by a user. In another example, a method oroperation can be triggered automatically.

Embodiments discussed herein related to distinguishing patients likelyto experience BCR from patients unlikely to experience BCR in CaP, togenerating a CaP BCR prognosis, generating a histotyping risk score, andother embodiments, are based on features that are not perceivable by thehuman eye, and their computation cannot be practically performed in thehuman mind. A machine learning classifier or deep learning model asdescribed herein cannot be implemented in the human mind or with penciland paper. Embodiments thus perform actions, steps, processes, or otheractions that are not practically performed in the human mind, at leastbecause they require a processor or circuitry to access digitized imagesstored in a computer memory and to extract or compute features, orcompute prognoses that are based on the digitized images and extractedfeatures and not on properties of tissue or the images that areperceivable by the human eye. Embodiments described herein can use acombined order of specific rules, elements, operations, circuits,logics, or components that render information into a specific formatthat can then be used and applied to create desired results moreaccurately, more consistently, and with greater reliability thanexisting approaches, thereby producing the technical effect of improvingthe performance of the machine, computer, or system with whichembodiments are implemented or practically integrated.

FIG. 17 illustrates an example apparatus 1700 that can facilitategenerating a BCR prognosis associated with a patient, or classifying apatient as BCR low-risk or BCR high-risk in CaP, based on digitizedpathology imagery, including for example, digitized H&E stained imagery,according to various embodiments discussed herein. Apparatus 1700 may beconfigured to perform various techniques, operations, or methodsdiscussed herein. Apparatus 1700 may be configured to, for example,train a deep learning model, a machine learning classifier (e.g.,quadratic discriminant analysis (QDA) classifier, linear discriminantanalysis (LDA) classifier, logistic regression model classifier, aconvolutional neural network (CNN) classifier, a support vector machine(SVM) classifier, etc.) based on training data to distinguish a patientas BCR low-risk or BCR high-risk in CaP, or employ such a trainedmachine learning classifier or deep learning model to generate aclassification of a patient based on QH features extracted fromdigitized H&E stained imagery, or further based on clinical dataassociated with the patient. Apparatus 1700 may be configured to, forexample, train a deep learning model to segment gland lumen representedin digitized H&E stained imagery. In one embodiment, apparatus 1700includes a processor 1710, and a memory 1720. Processor 1710 may, invarious embodiments, include circuitry such as, but not limited to, oneor more single-core or multi-core processors. Processor 1710 may includeany combination of general-purpose processors and dedicated processors(e.g., graphics processors, application processors, etc.). Theprocessor(s) can be coupled with and/or can comprise memory (e.g.,memory 1720) or storage and can be configured to execute instructionsstored in memory 1720 or storage to enable various apparatus,applications, or operating systems to perform operations and/or methodsdiscussed herein.

Memory 1720 is configured to store a digitized histopathology imageassociated with a patient, where the image includes a region of interest(ROI) demonstrating CaP. The digitized image may be, for example, adigitized H&E slide image. The digitized image has a plurality ofpixels, a pixel having an intensity. In some embodiments, memory 1720can store a training set or testing set of images. The training set ortesting set of images may for example, comprise digitized H&E imagesshowing CaP tissue, along with a known prognosis, or outcome (e.g., BCR,metastasis) for training a deep learning model or a classifier, forexample, a machine learning model, QDA classifier, etc., to segmentgland lumen represented in a digitized H&E stained image, or to generatea prognosis or determine a probability that the patient associated withthe image is BCR high-risk or BCR low-risk, while in the same or otherembodiments, memory 1720 can store a digitized H&E image of a patientfor whom a prediction of BCR, a prognosis, a classification, or outcomeis to be determined. Memory 1720 can be further configured to store oneor more clinical features or other data associated with the patientassociated with the digitized H&E image. For example, memory 1720 may beconfigured to store a Gleason grade group associated with the patient, aGleason score, or a pre-operation PSA level associated with the patient.

Apparatus 1700 also includes an input/output (I/O) interface 1730; a setof circuits 1750; and an interface 1740 that connects the processor1710, the memory 1720, the I/O interface 1730, and the set of circuits1750. I/O interface 1730 may be configured to transfer data betweenmemory 1720, processor 1710, circuits 1750, and external devices, forexample, a medical imaging device such as an digital whose slidescanner, system, or apparatus.

The set of circuits 1750 includes an image acquisition circuit 1751, asegmentation circuit 1752, a post-processing circuit 1754, aquantitative histomorphometry (QH) circuit 1755, a histotyping riskscore circuit 1756, a classification circuit 1757, a prognostic circuit1758, and a display circuit 1759.

Image acquisition circuit 1751 is configured to acquire a digitizedimage of a region of tissue demonstrating CaP pathology. In oneembodiment, image acquisition circuit 1751 is configured to acquire adigitized hematoxylin and eosin (H&E) stained image of a region oftissue demonstrating CaP pathology. The region of tissue includes atumor region. The digitized image includes a plurality of pixels, apixel having an intensity. The digitized image is associated with apatient. Acquiring or accessing the digitized H&E stained image mayinclude accessing a digitized H&E stained image stored in memory 1720.In another embodiment accessing the digitized H&E stained image mayinclude acquiring electronic data, reading from a computer file,receiving a computer file, reading from a computer memory, or othercomputerized activity not practically performed in the human mind.

Segmentation circuit 1752 is configured to generate a set of segmentedgland lumen. In one embodiment, the segmentation circuit comprises adeep learning segmentation model trained to segment gland lumenrepresented in a digitized H&E stained image of a region of tissuedemonstrating CaP. In one embodiment, the deep learning segmentationmodel is trained according to various techniques described herein. Forexample, the deep learning segmentation model may be trained accordingto operations 500. In one embodiment, the deep learning segmentationmodel is a modified UNet model. In one embodiment, segmentation circuit1752 is configured to access a deep learning segmentation model storedin, for example, memory 1720.

Post-processing circuit 1754 is configured to generate a set ofpost-processed segmented gland lumen by post-processing the set ofsegmented gland lumen. Post-processing circuit 1754 may be configured tocorrect, re-label, or remove objects incorrectly labelled as segmentedgland lumen from an initial post-processed set of segmented gland lumenaccording to techniques described herein. Post-processing circuit 1754may be configured to remove artifacts from the set of segmented glandlumen. In one embodiment, post-processing circuit 1754 is configured to:define a set of post-processed segmented gland lumen, where the set ofpost-processed segmented gland lumen includes the members of the set ofsegmented gland lumen; determine if a member of the set ofpost-processed segmented gland lumen includes a non-lumen region.Post-processing circuit 1754 is further configured to, upon determiningthat the member of the set of post-processed segmented gland lumenincludes a non-lumen region: re-label the non-lumen region as lumen.Post-processing circuit 1754 is further configured to determine an areaof a member of the set of segmented gland lumen. Post-processing circuit1754 is further configured to, upon determining that the member of theset of segmented gland lumen has an area less than a threshold area:remove the member of the set of segmented gland lumen from the set ofpost-processed segmented gland lumen; and determine a boundary of amember of the set of segmented gland lumen. Post-processing circuit 1754is further configured to, upon determining that the boundary of themember of the set of segmented gland lumen is defined by a white pixel:remove the segmented gland lumen from the set of post-processedsegmented gland lumen. In one embodiment, the threshold area is 4 μm².In another embodiment, the threshold area may have another, differentvalue, for example, 3 μm², or 5 μm², or other value.

Quantitative histomorphometry (QH) circuit 1755 is configured to extracta set of QH features based, at least in part, on the set ofpost-processed segmented gland lumen. QH circuit 1755 is also configuredto generate a feature vector based on the set of QH features. In oneembodiment, QH circuit 1755 is further configured to normalize thefeature vector according to various techniques described herein.

In one embodiment, the set of QH features includes at least ninefeatures. In this embodiment, the set of QH features includes a set ofgland lumen features, a set of sub-graph features, and a set of texturefeatures. In this embodiment, the set of gland lumen features is based,at least in part, on the post-processed set of segmented gland lumen. Inthis embodiment, the set of texture features includes at least onetexture feature extracted from the tumor region. In this embodiment, theset of sub-graph features includes at least one sub-graph featureextracted from the tumor region. In one embodiment, the set of QHfeatures includes at least seven gland lumen shape features, at leastone sub-graph feature, and at least one Haralick feature. In oneembodiment, the set of QH features includes a mean invariant moment 2feature, a mean Fourier descriptor 4 feature, a standard deviation ofsmoothness feature, a median distance ratio feature, a 5^(th)percentile/95^(th) percentile perimeter ratio feature, a 5^(th)percentile/95^(th) percentile Fourier descriptor 1 feature, a 5^(th)percentile/95^(th) percentile Fourier descriptor 6 feature, a skewnessof edge length sub-graph feature, and a Haralick mean correlationfeature. In another embodiment, QH circuit 1755 may be configured toextract another, different number of QH features, or the set of QHfeatures may include other, different QH features.

Histotyping risk score circuit 1756 is configured to compute a weightedsum of the feature vector. Histotyping risk score circuit 1756 is alsoconfigured to compute a histotyping risk score based on the weighted sumof the feature vector. In one embodiment, the histotyping risk score maybe within a range of, for example, [−0.72, 0.46]. In another embodiment,the histotyping risk score may be within another, different range, forexample, [−1, 1], or other range. In one embodiment, histotyping riskscore circuit 1756 is configured to compute the histotyping risk scorebased on the weighted sum of the feature vector, where the featurevector is normalized according to techniques described herein. In oneembodiment, histotyping risk score circuit 1756 is configured to computethe histotyping risk score by multiplying a normalized feature vector bya vector of β values. The vector of β values may be obtained from ahistotyping model trained according to techniques described herein. Inthis embodiment, histotyping risk score circuit 1756 is furtherconfigured to compute the histotyping risk score by computing the sum ofthe products of the normalized feature vector and their corresponding βvalues.

Classification circuit 1757 is configured to generate a classificationof the patient as biochemical recurrence (BCR) high-risk or BCR low-riskbased on the histotyping risk score and a risk score threshold. In thisembodiment, generating the classification based on the histotyping riskscore and the risk score threshold includes generating a categoricalclassification. For example, classification circuit 1757 may beconfigured to categorize the patient as BCR low-risk if the histotypingrisk score is less than (<) the risk score threshold value, or BCRhigh-risk if the histotyping risk score is than or equal to (>=) therisk score threshold value. In one embodiment, where the histotypingrisk score may be within a range of, for example, [−0.72, 0.46], therisk score threshold value may be, for example, 0.0960. In thisembodiment, a patient classified as BCR high-risk has a 1.95 timeshigher chance of BCR than a patient classified as BCR low-risk. Inanother embodiment, the risk score threshold value may be another,different value.

In various embodiments, the classification may include one or more of amost likely outcome (e.g., as determined based on the histotyping riskscore, the risk score threshold, the set of QH features) such membershipin a first class or second, different class, for example, BCR low-risk,BCR high-risk, or metastasis low-risk, metastasis high-risk, aprobability or confidence associated with a most likely outcome; and/orassociated probabilities/confidences associated with each of a pluralityof outcomes.

Prognostic circuit 1758 is configured to generate a first BCR prognosisbased, at least in part, on the classification. In one embodiment,prognostic circuit 1758 is further configured to generate a second BCRprognosis based on the histotyping risk score. In this embodiment,generating the second BCR prognosis based on the histotyping risk scoreincludes generating a BCR prognosis based on a continuousclassification. For example, when the histotyping risk score may bewithin a range of, for example, [−0.72, 0.46], an increase in thehistotyping risk score of 0.1 is associated with a prognosis of 1.05times higher risk of BCR. In another embodiment, the histotyping riskscore may be within another, different range, for example [−1, 1], orother range.

Display circuit 1759 is configured to display at least one of the BCRprognosis, the classification, the histotyping risk score, the weightedsum of the feature vector, the feature vector, the set of QH features,the set of post-processed segmented gland lumen, or the digitized image.In one embodiment, display circuit 1759 is further configured to displaythe second BCR prognosis according to techniques described herein.Display circuit 1759 may be further configured to optionally displayother data associated with the patient or associated with the operationof apparatus 1700.

FIG. 18 illustrates an apparatus 1800 that is similar to apparatus 1700but that includes additional elements and details. In one embodiment ofapparatus 1800, the set of circuits 1750 further includes a CaPpersonalized treatment plan circuit 1853. CaP personalized treatmentplan circuit 1853 is configured to generate a personalized CaP treatmentplan based, at least in part, on the classification. CaP personalizedtreatment plan circuit 1853 may be configured to generate a personalizedtreatment plan based, at least in part, on a classification obtainedfrom classification circuit 1757, or on a prognosis generated byprognostic circuit 1758. CaP personalized treatment plan circuit 1853may be configured to generate a personalized treatment plan for thepatient of whom the image was acquired based, at least in part, on theclassification derived therefrom. Defining a personalized treatment planfacilitates delivering a particular treatment that will betherapeutically active to the patient, while minimizing negative oradverse effects experienced by the patient. For example, thepersonalized CaP treatment plan may suggest a surgical treatment, maysuggest a pharmaceutical agent dosage or schedule, and/or othertreatments. Generating a personalized treatment plan based on a moreaccurate prognosis of BCR or a more accurate classification of a patientas BCR low-risk or BCR high-risk facilitates more efficient delivery ofcostly therapeutic or surgical treatments to patients more likely tobenefit from such treatments. For example, the personalized treatmentplan may suggest a first surgical treatment, may suggest a firstpharmaceutical agent dosage or schedule, and/or other treatments for apatient classified as a BCR low-risk, or may suggest a second, differentsurgical treatment or second, different pharmaceutical agent dosage orschedule or treatments for a patient classified as BCR high-risk. Inthis embodiment, display circuit 1759 is further configured tooptionally display the personalized treatment plan.

In one embodiment of apparatus 1800, the set of circuits 1750 furtherincludes a training and testing circuit 1851. Training and testingcircuit 1851 is configured to train classification circuit 1757, orsegmentation circuit 1752 on a training cohort according to variousembodiments described herein. Training and testing circuit 1851 is alsoconfigured to optionally test classification circuit 1757 on a testingcohort, according to various embodiments described herein.

In one embodiment, training and testing circuit 1851 is configured toselect a plurality of regions of interest (ROIs) from a set of digitizedH&E stained images. Training and testing circuit 1851 is also configuredto annotate a gland lumen represented in a member of the plurality ofROIs, and generate a first set of resized ROIs by resizing a member ofthe plurality of ROIs. Training and testing circuit 1851 is alsoconfigured to train a first deep learning model to segment a gland lumenbased on the first set of resized ROIs; evaluate the first deep learningmodel performance; and train a final deep learning model on a secondtraining set. In this embodiment, the second training set includes thefirst set of resized ROIs, and a second, different set of annotatedresized ROIs, where the second, different set of annotated resized ROIsis selected based on the evaluated first deep learning modelperformance. Training and testing circuit 1851 is further configured totest the final deep learning model.

In one embodiment, apparatus 1800 further includes a clinical featurecircuit 1855 configured to access clinical values associated with thepatient. In one embodiment, clinical feature circuit 1855 is configuredto access a pre-radical prostatectomy (RP) serum prostate specificantigen (PSA) level value associated with the patient. In thisembodiment, clinical feature circuit 1855 is also configured to access aGleason grade group value associated with the patient. In oneembodiment, the PSA level may be defined in ng/mL. In one embodiment,the Gleason grade group value may have a value of 1, 2, 3, 4, or 5. Inone embodiment, clinical feature circuit 1855 may be configured toaccess other, different clinical values associated with the patient,including, for example, values associated with surgical marginpositivity, number of positive lymph nodes, patient age, or seminalvesicle invasion status.

In this embodiment, apparatus 1800 further includes a histotyping-pluscircuit 1857. Histotyping-plus circuit 1857 is configured to compute ahistotyping-plus risk score based on the histotyping risk score, the PSAlevel value, and the Gleason grade group value. Histotyping-plus circuit1857 may be configured to compute the histotyping-plus risk scoreaccording to various techniques described herein. For example,histotyping-plus circuit 1857 may be configured to generate a secondfeature vector, where the second feature vector includes the value ofthe histotyping risk score, the pre-operative serum PSA level in ng/mL,and four binary variables corresponding to whether the patient wasassigned Gleason grade group 2, 3, 4, or 5, respectively. In thisexample, histotyping-plus circuit 1857 is also configured to multiplythe second feature vector by the β values from a trainedHistotyping-plus model according to techniques described herein. Thenonzero elements of the β vector correspond to Histotyping score,pre-operative PSA level, Gleason grade group 3, and Gleason grade group4. In this example, histotyping-plus circuit 1857 is further configuredto generate the histotyping-plus risk score by computing the sum of theproducts of the second feature vector and β values according to varioustechniques described herein. In another embodiment, the nonzero elementsof the β vector may correspond to other, different Gleason grade groups,or other different clinical factors, including, for example, surgicalmargin positivity, number of positive lymph nodes, patient age, orseminal vesicle invasion status.

In this embodiment, classification circuit 1757 is further configured togenerate a histotyping-plus classification of the patient as biochemicalrecurrence (BCR) high-risk or BCR low-risk based on the histotyping-plusrisk score and a histotyping-plus risk score threshold. For example,classification circuit 1757 may be configured to generate ahistotyping-plus classification of a patient having a histotyping-plusrisk score greater than the histotyping-plus risk score threshold as BCRhigh-risk, while a patient having a histotyping-plus risk score lessthan or equal to the histotyping-plus risk score threshold may beclassified as BCR low-risk. In one embodiment, the histotyping-plus riskscore threshold has a value of 1.331. In another embodiment, thehistotyping-plus risk score threshold may have another, different value.The histotyping-plus risk score threshold may be determined based, atleast in part, on a range selected from within the range thehistotyping-plus risk score is obtained, for example, [−1.210, 2.510],[−1.5, 3], [−0.5, 0.5], [−1, 1], or other range. For example, thehistotyping-plus risk score threshold may, in one embodiment, bedetermined as the threshold that maximizes the hazard ratio in BCR-freesurvival time between histotyping-plus low-risk and histotyping-plushigh-risk patients. In another embodiment, classification circuit 1757may be configured to generate the histotyping-plus classification usinganother, different classification scheme.

In this embodiment, prognostic circuit 1758 is further configured togenerate a histotyping-plus BCR prognosis. In this embodiment,prognostic circuit 1758 is configured to generate the histotyping-plusBCR prognosis based, at least in part, on the histotyping-plusclassification. For example, prognostic circuit 1758 may be configuredto generate a first histotyping-plus BCR prognosis associated with apatient classified as BCR high-risk, while prognostic circuit 1758 maybe configured to generate a second, different histotyping-plus BCRprognosis for a patient classified as BCR low-risk.

In this embodiment, display circuit 1759 is further configured todisplay the histotyping-plus BCR prognosis, the histotyping-plusclassification, or the histotyping-plus risk score, on a computermonitor, a smartphone display, a tablet display, or other displays,according to various techniques described herein.

In one embodiment, apparatus 1800 further includes personalized medicinedevice 1860. Apparatus 1800 may be configured to provide the histotypingrisk score, the histotyping-plus BCR prognosis, the classification, apersonalized CaP treatment plan, or other data to personalized medicinedevice 1860. Personalized medicine device 1860 may be, for example, acomputer assisted diagnosis (CADx) system or other type of personalizedmedicine device that can be used to facilitate the prediction of BCR inCaP, to facilitate generating a prognosis of BCR, or to facilitate theclassification of a patient as BCR low-risk or BCR high-risk. In oneembodiment, CaP personalized treatment plan circuit 1853 can controlpersonalized medicine device 1860 to display the histotyping risk score,the histotyping-plus BCR prognosis, the classification, the set of QHfeatures, the digitized H&E stained image, a prognosis, a personalizedtreatment plan, or other data to on a computer monitor, a smartphonedisplay, a tablet display, or other displays.

FIG. 19 illustrates an example computer 1900 in which example methodsillustrated herein can operate and in which example methods, apparatus,circuits, operations, or logics may be implemented. In differentexamples, computer 1900 may be part of CaP BCR prediction system orapparatus, a CaP classification system or apparatus, a CaP BCR prognosissystem, a CADx system, an MRI system, a CT system, a digital whole slidescanner, or a personalized medicine system, or may be operablyconnectable to a CaP BCR prediction system or apparatus, a CaPclassification system or apparatus, a CaP BCR prognosis system, a CADxsystem, an MRI system, a CT system, a digital whole slide scanner, or apersonalized medicine system.

Computer 1900 includes a processor 1902, a memory 1904, and input/output(I/O) ports 1910 operably connected by a bus 1908. In one example,computer 1900 may include a set of logics or circuits 1930 that performoperations for or a method of predicting BCR, generating a BCRprognosis, or classifying CaP patients as BCR low-risk or BCR high-riskbased on features extracted from digitized H&E stained imagery, orfurther based on clinical values associated with a patient, including byusing a machine learning classifier. Thus, the set of circuits 1930,whether implemented in computer 1900 as hardware, firmware, software,and/or a combination thereof may provide means (e.g., hardware,firmware, circuits) for predicting BCR in CaP, generating a BCRprognosis, or classifying CaP patients as BCR high-risk or BCR low-riskon digitized H&E stained imagery. In different examples, the set ofcircuits 1930 may be permanently and/or removably attached to computer1900.

Processor 1902 can be a variety of various processors including dualmicroprocessor and other multi-processor architectures. Processor 1902may be configured to perform steps of methods claimed and describedherein. Memory 1904 can include volatile memory and/or non-volatilememory. A disk 1906 may be operably connected to computer 1900 via, forexample, an input/output interface (e.g., card, device) 1918 and aninput/output port 1910. Disk 1906 may include, but is not limited to,devices like a magnetic disk drive, a tape drive, a Zip drive, a solidstate device, a flash memory card, or a memory stick. Furthermore, disk1906 may include optical drives like a CD-ROM or a digital video ROMdrive (DVD ROM). Memory 1904 can store processes 1914 or data 1917, forexample. Data 1917 may, in one embodiment, include digitized H&E stainedimages, including images of tissue demonstrating CaP. Data 1917 may, inone embodiment, also include clinical information associated with apatient, for example, PSA levels or Gleason grade scores. Disk 1906 ormemory 1904 can store an operating system that controls and allocatesresources of computer 1900.

Bus 1908 can be a single internal bus interconnect architecture or otherbus or mesh architectures. While a single bus is illustrated, it is tobe appreciated that computer 1900 may communicate with various devices,circuits, logics, and peripherals using other buses that are notillustrated (e.g., PCIE, SATA, Infiniband, 1394, USB, Ethernet).

Computer 1900 may interact with input/output devices via I/O interfaces1918 and input/output ports 1910. Input/output devices can include, butare not limited to, MRI systems, CT systems, digital whole slidescanners, an optical microscope, a keyboard, a microphone, a pointingand selection device, cameras, video cards, displays, disk 1906, networkdevices 1920, or other devices. Input/output ports 1910 can include butare not limited to, serial ports, parallel ports, or USB ports.

Computer 1900 may operate in a network environment and thus may beconnected to network devices 1920 via I/O interfaces 1918 or I/O ports1910. Through the network devices 1920, computer 1900 may interact witha network. Through the network, computer 1900 may be logically connectedto remote computers. The networks with which computer 1900 may interactinclude, but are not limited to, a local area network (LAN), a wide areanetwork (WAN), or other networks, including the cloud.

FIG. 20 illustrates a method or set of operations 2000 for generating abiochemical recurrence (BCR) prognosis associated with a patientdemonstrating prostate cancer (CaP) pathology. Operations 2000 includesacquiring electronic data, reading from a computer file, receiving acomputer file, reading from a computer memory, or other computerizedactivity not practically performed in the human mind. Operations 2000includes, at 2010, accessing a digitized hematoxylin and eosin (H&E)stained image of a region of tissue demonstrating CaP pathology. In oneembodiment, the region of tissue includes an annotated tumor region,where the digitized H&E stained image includes a plurality of pixels, apixel having an intensity, and where the digitized H&E stained image isassociated with a patient.

Operations 2000 also includes, at 2020, generating a set of segmentedgland lumen by segmenting a plurality of gland lumen represented in thetumor region using a deep learning segmentation model. The deep learningsegmentation model may be trained to segment gland lumen on a firsttraining set and further trained on a second training set that comprisesthe first training set and a second, different set of images, accordingto various techniques described herein.

Operations 2000 also includes, at 2024, generating a set ofpost-processed segmented gland lumen by re-labelling mis-labelled areasof a member of the set of segmented gland lumen, and by removingartifacts from the set of segmented gland lumen. The set ofpost-processed segmented gland lumen may be generated according tovarious techniques described herein, including, for example, operations400.

Operations 2000 also includes, at 2030, extracting a set of quantitativehistomorphometry (QH) features based, at least in part, on the set ofpost-processed segmented gland lumen. In one embodiment, the set of QHfeatures includes a set of gland lumen features, a set of sub-graphfeatures, and a set of texture features. In this embodiment, the set ofgland lumen features is based, at least in part, on the post-processedset of segmented gland lumen. In this embodiment, the set of texturefeatures includes at least one texture feature extracted from the tumorregion. In this embodiment, the set of sub-graph features includes atleast one sub-graph feature extracted from the tumor region. In oneembodiment, the set of QH features includes at least seven gland lumenshape features, at least one sub-graph feature, and at least oneHaralick feature. In one embodiment, the set of QH features includes amean invariant moment 2 feature, a mean Fourier descriptor 4 feature, astandard deviation of smoothness feature, a median distance ratiofeature, a 5%/95% perimeter ratio feature, a 5%/95% Fourier descriptor 1feature, a 5%/95% Fourier descriptor 6 feature, a skewness of edgelength sub-graph feature, and a Haralick mean correlation feature. Inanother embodiment, another, different number of QH features may beextracted, or the set of QH features may include other, different QHfeatures.

Operations 2000 also includes, at 2034, generating a feature vectorbased on the set of QH features. In one embodiment, generating thefeature vector includes normalizing the feature vector according tovarious techniques described herein. In one embodiment, the featurevector includes nine (9) elements.

Operations 2000 also includes, at 2040, computing a histotyping riskscore based on a weighted sum of the feature vector. The histotypingrisk score may be computed based on a weighted sum of the feature vectoraccording to various techniques described herein. For example, thehistotyping risk score may be computed by summing the products of thefeature vector or normalized feature vector and corresponding β valuesaccording to various techniques described herein.

Operations 2000 also includes, at 2050, generating a categoricalclassification of the patient as BCR high-risk or BCR low-risk based onthe histotyping risk score and a risk score threshold. For example, apatient associated with a histotyping risk score less than the riskscore threshold may be classified as BCR low-risk, while a patientassociated with a histotyping risk score greater than or equal to therisk score threshold may be classified as BCR high-risk. In oneembodiment, the histotyping risk score is within the range [−0.72,0.46], and the risk score threshold is 0.0960. In another embodiment,other ranges or risk score thresholds may be employed.

Operations 2000 also includes, at 2054, generating a continuousclassification of the patient based on the histotyping risk score. Forexample, a patient may be classified as more or less likely toexperience BCR if they are associated with a higher or lower histotypingrisk score, respectively.

Operations 2000 also includes, at 2060, generating a first BCR prognosisbased on the categorical classification. For example, in one embodiment,a first BCR prognosis comprising a first range of survival may begenerated based on a categorical classification of BCR high-risk, whilea first BCR prognosis comprising a second, different range of survivalmay be generated based on a categorical classification of BCR low-risk.

Operations 2000 also includes, at 2064, generating a second BCRprognosis based on the continuous classification. For example, in oneembodiment, a second BCR prognosis comprising a first range of survivalmay be generated based on a continuous classification associated with alower histotyping risk score, while a second BCR prognosis comprising asecond, different range of survival may be generated based on acontinuous classification associated with a higher histotyping riskscore.

Operations 2000 further includes, at 2070, displaying the first BCRprognosis or the second BCR prognosis. Displaying the first BCRprognosis or the second BCR prognosis may include displaying the firstBCR prognosis or the second BCR prognosis on a computer monitor, asmartphone display, a tablet display, or other displays. Displaying thefirst BCR prognosis or the second BCR prognosis may also includeprinting the first BCR prognosis or the second BCR prognosis.

In one embodiment, a non-transitory computer-readable storage device isconfigured to store instructions that when executed control a processorto perform operations that facilitate generating a prognosis of BCRassociated with a patient. FIG. 22 illustrates an example set ofoperations 2200. Operations 2200 includes, at 2210 accessing a digitizedimage of a region of tissue demonstrating prostate cancer (CaP)pathology, where the region of tissue includes a tumor region, where thedigitized image includes a plurality of pixels, a pixel having anintensity. In one embodiment, the digitized image is a digitized imageof a hematoxylin and eosin (H&E) stained whole slide image (WSI)acquired post-radical prostatectomy (RP). The digitized image isassociated with a patient. In one embodiment, the digitized imageincludes an annotated tumor region. In another embodiment, theoperations 2200 further comprise automatically annotating the tumorregion. Operations 2200 includes acquiring electronic data, reading froma computer file, receiving a computer file, reading from a computermemory, or other computerized activity not practically performed in thehuman mind.

Operations 2200 also includes, at 2220, generating a set of segmentedgland lumen by segmenting a plurality of gland lumen represented in thetumor region using a deep learning segmentation model. In oneembodiment, the deep learning segmentation model is trained to segmentgland lumen represented in H&E stained imagery. In this embodiment,training the deep learning segmentation model includes: selecting aplurality of ROIs from a set of digitized H&E stained images; annotatinga gland lumen represented in a member of the plurality of ROIs;generating a first set of resized ROIs by resizing a member of theplurality of ROIs; training a first deep learning model to segment agland lumen based on the first set of resized ROIs; evaluating the firstdeep learning model performance; training a final deep learning model ona second training set, where the second training set includes the firstset of resized ROIs, and a second, different set of annotated resizedROIs, where the second, different set of annotated resized ROIs isselected based on the evaluated first deep learning model performance;and testing the final deep learning model.

Operations 2200 also includes, at 2230, generating a set ofpost-processed segmented gland lumen by post-processing the set ofsegmented gland lumen. In one embodiment, post-processing the set ofsegmented gland lumen comprises: defining a set of post-processedsegmented gland lumen, where the set of post-processed segmented glandlumen includes the members of the set of segmented gland lumen;determining if a member of the set of post-processed segmented glandlumen includes a non-lumen region; upon determining that the member ofthe set of post-processed segmented gland lumen includes a non-lumenregion: re-labelling the non-lumen region as lumen. In this embodiment,post-processing the set of segmented gland lumen also comprisesdetermining an area of a member of the set of segmented gland lumen;upon determining that the member of the set of segmented gland lumen hasan area less than a threshold area: removing the member of the set ofsegmented gland lumen from the set of post-processed segmented glandlumen. In one embodiment, the threshold area is 4 μm². In thisembodiment, post-processing the set of segmented gland lumen furthercomprises determining a boundary of a member of the set of segmentedgland lumen; upon determining that the boundary of the member of the setof segmented gland lumen is defined by a white pixel: removing thesegmented gland lumen from the set of post-processed segmented glandlumen. In one embodiment, determining that the boundary of the member ofthe set of segmented gland lumen is defined by a white pixel includesdetermining if more than a threshold percentage of pixels, for example,5% of the pixels in the dilated boundary, are white. In anotherembodiment, other threshold levels, for example, 2%, or 10%, may beemployed.

Operations 2200 also includes, at 2240, extracting a set of quantitativehistomorphometry (QH) features from the digitized image based, at leastin part, on the set of post-processed segmented gland lumen. In oneembodiment, the set of QH features includes a mean invariant moment 2feature, a mean Fourier descriptor 4 feature, a standard deviation ofsmoothness feature, a median distance ratio feature, a 5^(th)percentile/95^(th) percentile perimeter ratio feature, a 5^(th)percentile/95^(th) percentile Fourier descriptor 1 feature, a 5^(th)percentile/95^(th) percentile Fourier descriptor 6 feature, a skewnessof edge length sub-graph feature, and a Haralick mean correlationfeature. In one embodiment, the set of QH features includes nine (9) QHfeatures. In another embodiment, the set of QH features may includeanother, different number of features.

Operations 2200 also includes, at 2242, generating a first featurevector based on the set of QH features. In one embodiment, generatingthe first feature vector includes normalizing the first feature vectoraccording to various techniques described herein.

Operations 2200 also includes, at, 2250, computing a continuoushistotyping risk score based on a weighted sum of the first featurevector. In one embodiment, In one embodiment, the continuous histotypingrisk score is a unbounded value. In another embodiment, the continuoushistotyping risk score is within a range, for example [−1, 1], [−0.76 to0.42], or other range.

Operations 2200 also includes, at 2260, accessing a pre-radicalprostatectomy (RP) serum prostate specific antigen (PSA) level valueassociated with the patient. In one embodiment, the PSA level ismeasured in ng/mL.

Operations 2200 also includes, at 2262, accessing a Gleason grade valueassociated with the patient. In one embodiment, the Gleason grade valueis one of Gleason grade group 1, 2, 3, 4, or 5.

Operations 2200 also includes, at 2264, computing a histotyping-plusrisk score based on the continuous histotyping risk score, the PSA levelvalue, and the Gleason grade value. In one embodiment, computing thehistotyping-plus risk score includes generating a second feature vector,where the second feature vector includes the value of the histotypingrisk score, the pre-operative serum PSA level in ng/mL, and four binaryvariables corresponding to whether the patient was assigned Gleasongrade group 2, 3, 4, or 5, respectively. The second feature vector ismultiplied by the β values from a trained Histotyping-plus modelaccording to techniques described herein. In one embodiment, the nonzeroelements of the β vector correspond to Histotyping score, pre-operativePSA level, Gleason grade group 3, and Gleason grade group 4. The sum ofthe products of the feature vector and β values is the Histotyping-plusrisk score.

Operations 2200 also includes, at 2270, generating a classification ofthe patient as biochemical recurrence (BCR) high-risk or BCR low-riskbased on the histotyping-plus risk score, and a histotyping-plus riskscore threshold. In one embodiment, the histotyping-plus risk scorethreshold has a value of 1.331. In another embodiment, another,different histotyping-plus risk score threshold may be employed. Inanother embodiment, another, different classification scheme may beemployed.

Operations 2200 also includes, at 2280, generating a BCR prognosisbased, at least in part, on the classification. In one embodiment, forexample, a first BCR prognosis comprising a first range of survival maybe generated for a patient classified as BCR high-risk, while a second,different BCR prognosis comprising a second, different range of survivalmay be generated for a patient classified as BCR low-risk.

Operations 2200 further includes, at 2290, displaying the BCR prognosis.Displaying the BCR prognosis may include displaying the BCR prognosis ona computer monitor, a smartphone display, a tablet display, or otherdisplays. In one embodiment, displaying the BCR prognosis may furtherinclude optionally displaying the classification, the histotyping-plusrisk score, the continuous histotyping risk score, the PSA level value,the Gleason grade value, the set of QH features, the set ofpost-processed segmented gland lumen, the set of segmented gland lumen,or the digitized image.

In one embodiment, operations 2200 may further include generating apersonalized CaP treatment plan based on at least one of the BCRprognosis, the classification, or the histotyping-plus risk score; andoptionally displaying the personalized CaP treatment plan. Operations2200 may further include displaying the CaP treatment plan on a computermonitor, a smartphone display, a tablet display, or other displaysaccording to various techniques described herein.

Examples herein can include subject matter such as an apparatus, adigital whole slide scanner, an MRI system, a CT system, an opticalmicroscopy system, a personalized medicine system, a CADx system, aprocessor, a system, circuitry, operations, a method, means forperforming operations, acts, steps, or blocks of the method, at leastone machine-readable medium including executable instructions that, whenperformed by a machine (e.g., a processor with memory, anapplication-specific integrated circuit (ASIC), a field programmablegate array (FPGA), or the like) cause the machine to perform acts of themethod or of an apparatus or system for classifying a patient aslow-risk or as high-risk of BCR in CaP, or generating a BCR prognosis,according to embodiments and examples described.

Example 1 is a non-transitory computer-readable storage device storingcomputer-executable instructions that when executed cause a processor toperform operations, the operations comprising: accessing a digitizedimage of a region of tissue demonstrating prostate cancer (CaP)pathology, where the region of tissue includes a tumor region, where thedigitized image includes a plurality of pixels, a pixel having anintensity, and where the digitized image is associated with a patient;generating a set of segmented gland lumen by segmenting a plurality ofgland lumen represented in the tumor region using a deep learningsegmentation model; generating a set of post-processed segmented glandlumen by post-processing the set of segmented gland lumen; extracting aset of quantitative histomorphometry (QH) features from the digitizedimage based, at least in part, on the set of post-processed segmentedgland lumen; generating a feature vector based on the set of QHfeatures; computing a histotyping risk score based on a weighted sum ofthe feature vector; generating a classification of the patient asbiochemical recurrence (BCR) high-risk or BCR low-risk based on thehistotyping risk score and a risk score threshold; generating a BCRprognosis based, at least in part, on the classification; and displayingthe BCR prognosis.

Example 2 comprises the subject matter of any variation of any ofexample(s) 1, where the digitized image is a digitized image of ahematoxylin and eosin (H&E) stained tissue slide of a region of tissuedemonstrating CaP pathology.

Example 3 comprises the subject matter of any variation of any ofexample(s) 1-2, where the digitized image includes an annotated tumorregion.

Example 4 comprises the subject matter of any variation of any ofexample(s) 1-3, the operations further comprising automaticallyannotating the tumor region.

Example 5 comprises the subject matter of any variation of any ofexample(s) 1-4, where generating the post-processed set of segmentedgland lumen by post-processing the set of segmented gland lumencomprises: defining a set of post-processed segmented gland lumen, wherethe set of post-processed segmented gland lumen includes the members ofthe set of segmented gland lumen; determining if a member of the set ofpost-processed segmented gland lumen includes a non-lumen region; upondetermining that the member of the set of post-processed segmented glandlumen includes a non-lumen region: re-labelling the non-lumen region aslumen; determining an area of a member of the set of segmented glandlumen; upon determining that the member of the set of segmented glandlumen has an area less than a threshold area: removing the member of theset of segmented gland lumen from the set of post-processed segmentedgland lumen; and determining a boundary of a member of the set ofsegmented gland lumen; upon determining that the boundary of the memberof the set of segmented gland lumen is defined by a white pixel:removing the segmented gland lumen from the set of post-processedsegmented gland lumen.

Example 6 comprises the subject matter of any variation of any ofexample(s) 1-5, where the threshold area is 4 μm².

Example 7 comprises the subject matter of any variation of any ofexample(s) 1-6, where the set of QH features includes a set of glandlumen shape features, a set of sub-graph features, and a set of texturefeatures.

Example 8 comprises the subject matter of any variation of any ofexample(s) 1-7, where the set of gland lumen shape features is based, atleast in part, on the post-processed set of segmented gland lumen, wherethe set of gland lumen shape features includes at least seven glandlumen shape features; where the set of sub-graph features includes atleast one sub-graph feature based on the set of segmented gland lumen;and where the set of texture features includes at least one texturefeature extracted from the tumor region.

Example 9 comprises the subject matter of any variation of any ofexample(s) 1-8, where the set of gland lumen shape features includes: amean invariant moment 2 feature, a mean Fourier descriptor 4 feature, astandard deviation of smoothness feature, a median distance ratiofeature, a 5^(th) percentile/95^(th) percentile perimeter ratio feature,a 5^(th) percentile/95^(th) percentile Fourier descriptor 1 feature, a5^(th) percentile/95^(th) percentile Fourier descriptor 6 feature; wherethe set of sub-graph features includes a skewness of edge lengthsub-graph feature; and where the set of texture features includes aHaralick mean correlation feature.

Example 10 comprises the subject matter of any variation of any ofexample(s) 1-9, where segmenting a gland lumen using a deep learningsegmentation model includes segmenting a gland lumen using a deeplearning model trained to segment gland lumen represented in a digitizedhematoxylin and eosin (H&E) stained image of a region of tissuedemonstrating CaP.

Example 11 comprises the subject matter of any variation of any ofexample(s) 1-10, where training the deep learning model comprises:selecting a plurality of regions of interest (ROIs) from a set ofdigitized H&E stained images; annotating a gland lumen represented in amember of the plurality of ROIs; generating a first set of resized ROIsby resizing a member of the plurality of ROIs; training a first deeplearning model to segment a gland lumen based on the first set ofresized ROIs; evaluating the first deep learning model performance;training a final deep learning model on a second training set, where thesecond training set includes the first set of resized ROIs, and asecond, different set of annotated resized ROIs, where the second,different set of annotated resized ROIs is selected based on theevaluated first deep learning model performance; and testing the finaldeep learning model.

Example 12 comprises the subject matter of any variation of any ofexample(s) 1-11, where generating the set of resized ROIs comprisesresizing a member of the plurality of ROIs to one μm per pixel.

Example 13 comprises the subject matter of any variation of any ofexample(s) 1-12, the operations further comprising: generating a secondBCR prognosis based, at least in part, on the histotyping risk score;and displaying the second BCR prognosis.

Example 14 comprises the subject matter of any variation of any ofexample(s) 1-13, the operations further comprising generating apersonalized CaP treatment plan based on at least one of the BCRprognosis, the second BCR prognosis, the classification, or thehistotyping risk score; and optionally displaying the personalized CaPtreatment plan.

Example 15 comprises the subject matter of any variation of any ofexample(s) 1-14, the operations further comprising: accessing apre-radical prostatectomy (RP) serum prostate specific antigen (PSA)level value associated with the patient; accessing a Gleason grade groupvalue associated with the patient; computing a histotyping-plus riskscore based on the histotyping risk score, the PSA level value, and theGleason grade group value; generating a second classification of thepatient as biochemical recurrence (BCR) high-risk or BCR low-risk basedon the histotyping-plus risk score, and a histotyping-plus risk scorethreshold; generating a histotyping-plus BCR prognosis based, at leastin part, on the classification; and displaying the histotyping-plus BCRprognosis.

Example 16 comprises an apparatus comprising: a processor; a memoryconfigured to store a digitized image of a region of tissuedemonstrating prostate cancer (CaP) pathology; an input/output (I/O)interface; a set of circuits; and an interface that connects theprocessor, the memory, the I/O interface, and the set of circuits, theset of circuits comprising: an image acquisition circuit configured toacquire a digitized hematoxylin and eosin (H&E) stained image of aregion of tissue demonstrating CaP pathology, where the region of tissueincludes a tumor region, where the region of tissue includes a pluralityof gland lumen, where the digitized H&E stained image includes aplurality of pixels, a pixel having an intensity, and where thedigitized H&E stained image is associated with a patient; a segmentationcircuit configured to generate a set of segmented gland lumen based onthe digitized H&E stained image, where the segmentation circuitcomprises a deep learning segmentation model trained to segment glandlumen represented in a digitized H&E stained image of a region of tissuedemonstrating CaP; a post-processing circuit configured to generate aset of post-processed segmented gland lumen by post-processing the setof segmented gland lumen; a quantitative histomorphometry (QH) circuitconfigured to: extract a set of QH features based, at least in part, onthe set of post-processed segmented gland lumen; and generate a featurevector based on the set of QH features; a histotyping risk score circuitconfigured to: compute a weighted sum of the feature vector; and computea histotyping risk score based on the weighted sum of the featurevector; a classification circuit configured to generate a classificationof the patient as biochemical recurrence (BCR) high-risk or BCR low-riskbased on the histotyping risk score and a risk score threshold; aprognostic circuit configured to generate a first BCR prognosis based,at least in part, on the classification, and a second BCR prognosisbased on the histotyping risk score; and a display circuit configured todisplay at least one of the first BCR prognosis, the second BCRprognosis, the classification, the histotyping risk score, the weightedsum of the feature vector, the feature vector, the set of QH features,the set of post-processed segmented gland lumen, or the digitized image.

Example 17 comprises the subject matter of any variation of any ofexample(s) 16, where the post-processing circuit is configured to:define a set of post-processed segmented gland lumen, where the set ofpost-processed segmented gland lumen includes the members of the set ofsegmented gland lumen; determine if a member of the set ofpost-processed segmented gland lumen includes a non-lumen region; upondetermining that the member of the set of post-processed segmented glandlumen includes a non-lumen region: re-label the non-lumen region aslumen; determine an area of a member of the set of segmented glandlumen; upon determining that the member of the set of segmented glandlumen has an area less than a threshold area: remove the member of theset of segmented gland lumen from the set of post-processed segmentedgland lumen; and determine a boundary of a member of the set ofsegmented gland lumen; upon determining that the boundary of the memberof the set of segmented gland lumen is defined by a white pixel: removethe segmented gland lumen from the set of post-processed segmented glandlumen.

Example 18 comprises the subject matter of any variation of any ofexample(s) 16-17, the set of circuits further comprising a trainingcircuit configured to: select a plurality of regions of interest (ROIs)from a set of digitized H&E stained images; annotate a gland lumenrepresented in a member of the plurality of ROIs; generate a first setof resized ROIs by resizing a member of the plurality of ROIs; train afirst deep learning model to segment a gland lumen based on the firstset of resized ROIs; evaluate the first deep learning model performance;train a final deep learning model on a second training set, where thesecond training set includes the first set of resized ROIs, and asecond, different set of annotated resized ROIs, where the second,different set of annotated resized ROIs is selected based on theevaluated first deep learning model performance; and test the final deeplearning model.

Example 19 comprises the subject matter of any variation of any ofexample(s) 16-18, where the classification circuit is further configuredto generate a second BCR prognosis based, at least in part, on thehistotyping risk score; and where the display circuit is furtherconfigured to display the second BCR prognosis.

Example 20 comprises the subject matter of any variation of any ofexample(s) 16-19, the set of circuits further comprising: a clinicalfeature circuit configured to: access a pre-radical prostatectomy (RP)serum prostate specific antigen (PSA) level value associated with thepatient; and access a Gleason grade group value associated with thepatient; and a histotyping-plus circuit configured to: compute ahistotyping-plus risk score based on the histotyping risk score, the PSAlevel value, and the Gleason grade group value; where the classificationcircuit is further configured to generate a histotyping-plusclassification of the patient as biochemical recurrence (BCR) high-riskor BCR low-risk based on the histotyping-plus risk score and ahistotyping-plus risk score threshold; where the prognostic circuit isfurther configured to generate a histotyping-plus BCR prognosis based,at least in part, on the histotyping-plus classification; and where thedisplay circuit is further configured to display the histotyping-plusBCR prognosis, the histotyping-plus classification, or thehistotyping-plus risk score.

Example 21 comprises a non-transitory computer-readable storage devicestoring computer-executable instructions that when executed cause aprocessor to perform operations for generating a biochemical recurrenceprognosis associated with a patient demonstrating prostate cancer (CaP)pathology, the operations comprising: accessing a digitized hematoxylinand eosin (H&E) stained image of a region of tissue demonstrating CaPpathology, where the region of tissue includes an annotated tumorregion, where the digitized H&E stained image includes a plurality ofpixels, a pixel having an intensity, and where the digitized H&E stainedimage is associated with a patient; generating a set of segmented glandlumen by segmenting a plurality of gland lumen represented in the tumorregion using a deep learning segmentation model trained to segment glandlumen on a first training set, and further trained on a second trainingset that comprises the first training set and a second, different set ofimages; generating a set of post-processed segmented gland lumen byre-labelling mis-labelled areas of a member of the set of segmentedgland lumen, and by removing artifacts from the set of segmented glandlumen; extracting a set of quantitative histomorphometry (QH) featuresbased, at least in part, on the set of post-processed segmented glandlumen, where the set of QH features includes a set of gland lumenfeatures and a set of texture features, where the set of gland lumenfeatures includes at least two lumen arrangement features, at least onelumen orientation disorder feature, and at least ten lumen shapefeatures, and where the set of texture features includes at least twotexture features extracted from the tumor region; generating a featurevector based on the set of QH features; computing a histotyping riskscore based on a weighted sum of the feature vector; generating acategorical classification of the patient as biochemical recurrence(BCR) high-risk or BCR low-risk based on the histotyping risk score anda risk score threshold; generating a continuous classification of thepatient as BCR high-risk or BCR low-risk based on the histotyping riskscore; generating a first BCR prognosis based on the categoricalclassification; generating a second BCR prognosis based on continuousclassification; and displaying the first BCR prognosis or the second BCRprognosis.

Example 22 comprises a non-transitory computer-readable storage devicestoring computer-executable instructions that when executed cause aprocessor to perform operations, the operations comprising: accessing adigitized image of a region of tissue demonstrating prostate cancer(CaP) pathology, where the region of tissue includes a tumor region,where the digitized image includes a plurality of pixels, a pixel havingan intensity, where the digitized image is a digitized image of ahematoxylin and eosin (H&E) stained whole slide image (WSI) acquiredpost-radical prostatectomy (RP), and where the digitized image isassociated with a patient; generating a set of segmented gland lumen bysegmenting a plurality of gland lumen represented in the tumor regionusing a deep learning segmentation model; generating a set ofpost-processed segmented gland lumen by post-processing the set ofsegmented gland lumen; extracting a set of quantitative histomorphometry(QH) features from the digitized image based, at least in part, on theset of post-processed segmented gland lumen; generating a first featurevector based on the set of QH features; computing a continuoushistotyping risk score based on a weighted sum of the first featurevector; accessing a pre-RP serum prostate specific antigen (PSA) levelvalue associated with the patient; accessing a Gleason grade group valueassociated with the patient; computing a histotyping-plus risk scorebased on the continuous histotyping risk score, the PSA level value, andthe Gleason grade group value; generating a classification of thepatient as biochemical recurrence (BCR) high-risk or BCR low-risk basedon the histotyping-plus risk score, and a histotyping-plus risk scorethreshold; generating a BCR prognosis based, at least in part, on theclassification; and displaying the BCR prognosis.

Example 23 comprises the subject matter of any variation of any ofexample(s) 22, where the digitized image includes an annotated tumorregion.

Example 24 comprises the subject matter of any variation of any ofexample(s) 22-23, the operations further comprising automaticallyannotating the tumor region.

Example 25 comprises the subject matter of any variation of any ofexample(s) 22-24, where generating the post-processed set of segmentedgland lumen by post-processing the set of segmented gland lumencomprises: defining a set of post-processed segmented gland lumen, wherethe set of post-processed segmented gland lumen includes the members ofthe set of segmented gland lumen; determining if a member of the set ofpost-processed segmented gland lumen includes a non-lumen region; upondetermining that the member of the set of post-processed segmented glandlumen includes a non-lumen region: re-labelling the non-lumen region aslumen; determining an area of a member of the set of segmented glandlumen; upon determining that the member of the set of segmented glandlumen has an area less than a threshold area: removing the member of theset of segmented gland lumen from the set of post-processed segmentedgland lumen; and determining a boundary of a member of the set ofsegmented gland lumen; upon determining that the boundary of the memberof the set of segmented gland lumen is defined by a white pixel:removing the segmented gland lumen from the set of post-processedsegmented gland lumen.

Example 26 comprises the subject matter of any variation of any ofexample(s) 22-25, where the threshold area is 4 μm².

Example 27 comprises the subject matter of any variation of any ofexample(s) 22-26, where the set of QH features includes a mean invariantmoment 2 feature, a mean Fourier descriptor 4 feature, a standarddeviation of smoothness feature, a median distance ratio feature, a5^(th) percentile/95^(th) percentile perimeter ratio feature, a 5^(th)percentile/95^(th) percentile Fourier descriptor 1 feature, a 5^(th)percentile/95^(th) percentile Fourier descriptor 6 feature, a skewnessof edge length sub-graph feature, and a Haralick mean correlationfeature.

Example 28 comprises a machine readable storage device that storesinstructions for execution by a processor to perform any of thedescribed operations of examples 1-27.

Example 29 comprises an apparatus comprising: a memory; and one or moreprocessors configured to: perform any of the described operations ofexamples 1-27.

References to “one embodiment”, “an embodiment”, “one example”, and “anexample” indicate that the embodiment(s) or example(s) so described mayinclude a particular feature, structure, characteristic, property,element, or limitation, but that not every embodiment or examplenecessarily includes that particular feature, structure, characteristic,property, element or limitation. Furthermore, repeated use of the phrase“in one embodiment” does not necessarily refer to the same embodiment,though it may.

“Computer-readable storage device”, as used herein, refers to a devicethat stores instructions or data. “Computer-readable storage device”does not refer to propagated signals. A computer-readable storage devicemay take forms, including, but not limited to, non-volatile media, andvolatile media. Non-volatile media may include, for example, opticaldisks, magnetic disks, tapes, and other media. Volatile media mayinclude, for example, semiconductor memories, dynamic memory, and othermedia. Common forms of a computer-readable storage device may include,but are not limited to, a floppy disk, a flexible disk, a hard disk, amagnetic tape, other magnetic medium, an application specific integratedcircuit (ASIC), a compact disk (CD), other optical medium, a randomaccess memory (RAM), a read only memory (ROM), a memory chip or card, amemory stick, and other media from which a computer, a processor orother electronic device can read.

“Circuit”, as used herein, includes but is not limited to hardware,firmware, software in execution on a machine, or combinations of each toperform a function(s) or an action(s), or to cause a function or actionfrom another logic, method, or system. A circuit may include a softwarecontrolled microprocessor, a discrete logic (e.g., ASIC), an analogcircuit, a digital circuit, a programmed logic device, a memory devicecontaining instructions, and other physical devices. A circuit mayinclude one or more gates, combinations of gates, or other circuitcomponents. Where multiple logical circuits are described, it may bepossible to incorporate the multiple logical circuits into one physicalcircuit. Similarly, where a single logical circuit is described, it maybe possible to distribute that single logical circuit between multiplephysical circuits.

To the extent that the term “includes” or “including” is employed in thedetailed description or the claims, it is intended to be inclusive in amanner similar to the term “comprising” as that term is interpreted whenemployed as a transitional word in a claim.

Throughout this specification and the claims that follow, unless thecontext requires otherwise, the words ‘comprise’ and ‘include’ andvariations such as ‘comprising’ and ‘including’ will be understood to beterms of inclusion and not exclusion. For example, when such terms areused to refer to a stated integer or group of integers, such terms donot imply the exclusion of any other integer or group of integers.

To the extent that the term “or” is employed in the detailed descriptionor claims (e.g., A or B) it is intended to mean “A or B or both”. Whenthe applicants intend to indicate “only A or B but not both” then theterm “only A or B but not both” will be employed. Thus, use of the term“or” herein is the inclusive, and not the exclusive use. See, Bryan A.Garner, A Dictionary of Modern Legal Usage 624 (2d. Ed. 1995).

While example systems, methods, and other embodiments have beenillustrated by describing examples, and while the examples have beendescribed in considerable detail, it is not the intention of theapplicants to restrict or in any way limit the scope of the appendedclaims to such detail. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the systems, methods, and other embodiments described herein.Therefore, the invention is not limited to the specific details, therepresentative apparatus, and illustrative examples shown and described.Thus, this application is intended to embrace alterations,modifications, and variations that fall within the scope of the appendedclaims.

What is claimed is:
 1. A non-transitory computer-readable storage devicestoring computer-executable instructions that when executed cause aprocessor to perform operations, the operations comprising: accessing adigitized image of a region of tissue demonstrating prostate cancer(CaP) pathology, where the region of tissue includes a tumor region,where the digitized image includes a plurality of pixels, a pixel havingan intensity, and where the digitized image is associated with apatient; generating a set of segmented gland lumen by segmenting aplurality of gland lumen represented in the tumor region using a deeplearning segmentation model; generating a set of post-processedsegmented gland lumen by post-processing the set of segmented glandlumen; extracting a set of quantitative histomorphometry (QH) featuresfrom the digitized image based, at least in part, on the set ofpost-processed segmented gland lumen; generating a feature vector basedon the set of QH features; computing a histotyping risk score based on aweighted sum of the feature vector; generating a classification of thepatient as biochemical recurrence (BCR) high-risk or BCR low-risk basedon the histotyping risk score and a risk score threshold; generating aBCR prognosis based, at least in part, on the classification; anddisplaying the BCR prognosis.
 2. The non-transitory computer-readablestorage device of claim 1, where the digitized image is a digitizedimage of a hematoxylin and eosin (H&E) stained tissue slide of a regionof tissue demonstrating CaP pathology.
 3. The non-transitorycomputer-readable storage device of claim 1, where the digitized imageincludes an annotated tumor region.
 4. The non-transitorycomputer-readable storage device of claim 1, the operations furthercomprising automatically annotating the tumor region.
 5. Thenon-transitory computer-readable storage device of claim 1, wheregenerating the post-processed set of segmented gland lumen bypost-processing the set of segmented gland lumen comprises: defining aset of post-processed segmented gland lumen, where the set ofpost-processed segmented gland lumen includes the members of the set ofsegmented gland lumen; determining if a member of the set ofpost-processed segmented gland lumen includes a non-lumen region; upondetermining that the member of the set of post-processed segmented glandlumen includes a non-lumen region: re-labelling the non-lumen region aslumen; determining an area of a member of the set of segmented glandlumen; upon determining that the member of the set of segmented glandlumen has an area less than a threshold area: removing the member of theset of segmented gland lumen from the set of post-processed segmentedgland lumen; and determining a boundary of a member of the set ofsegmented gland lumen; upon determining that the boundary of the memberof the set of segmented gland lumen is defined by a white pixel:removing the segmented gland lumen from the set of post-processedsegmented gland lumen.
 6. The non-transitory computer-readable storagedevice of claim 5, where the threshold area is 4 μm².
 7. Thenon-transitory computer-readable storage device of claim 1, where theset of QH features includes a set of gland lumen shape features, a setof sub-graph features, and a set of texture features.
 8. Thenon-transitory computer-readable storage device of claim 7, where theset of gland lumen shape features is based, at least in part, on thepost-processed set of segmented gland lumen, where the set of glandlumen shape features includes at least seven gland lumen shape features;where the set of sub-graph features includes at least one sub-graphfeature based on the set of segmented gland lumen; and where the set oftexture features includes at least one texture feature extracted fromthe tumor region.
 9. The non-transitory computer-readable storage deviceof claim 8, where the set of gland lumen shape features includes: a meaninvariant moment 2 feature, a mean Fourier descriptor 4 feature, astandard deviation of smoothness feature, a median distance ratiofeature, a 5^(th) percentile/95^(th) percentile perimeter ratio feature,a 5^(th) percentile/95^(th) percentile Fourier descriptor 1 feature, a5^(th) percentile/95^(th) percentile Fourier descriptor 6 feature; wherethe set of sub-graph features includes a skewness of edge lengthsub-graph feature; and where the set of texture features includes aHaralick mean correlation feature.
 10. The non-transitorycomputer-readable storage device of claim 1, where segmenting a glandlumen using a deep learning segmentation model includes segmenting agland lumen using a deep learning model trained to segment gland lumenrepresented in a digitized hematoxylin and eosin (H&E) stained image ofa region of tissue demonstrating CaP.
 11. The non-transitorycomputer-readable storage device of claim 10, where training the deeplearning model comprises: selecting a plurality of regions of interest(ROIs) from a set of digitized H&E stained images; annotating a glandlumen represented in a member of the plurality of ROIs; generating afirst set of resized ROIs by resizing a member of the plurality of ROIs;training a first deep learning model to segment a gland lumen based onthe first set of resized ROIs; evaluating the first deep learning modelperformance; training a final deep learning model on a second trainingset, where the second training set includes the first set of resizedROIs, and a second, different set of annotated resized ROIs, where thesecond, different set of annotated resized ROIs is selected based on theevaluated first deep learning model performance; and testing the finaldeep learning model.
 12. The non-transitory computer-readable storagedevice of claim 11, where generating the set of resized ROIs comprisesresizing a member of the plurality of ROIs to one μm per pixel.
 13. Thenon-transitory computer-readable storage device of claim 1, theoperations further comprising: generating a second BCR prognosis based,at least in part, on the histotyping risk score; and displaying thesecond BCR prognosis.
 14. The non-transitory computer-readable storagedevice of claim 13, the operations further comprising generating apersonalized CaP treatment plan based on at least one of the BCRprognosis, the second BCR prognosis, the classification, or thehistotyping risk score; and optionally displaying the personalized CaPtreatment plan.
 15. The non-transitory computer-readable storage deviceof claim 1, the operations further comprising: accessing a pre-radicalprostatectomy (RP) serum prostate specific antigen (PSA) level valueassociated with the patient; accessing a Gleason grade group valueassociated with the patient; computing a histotyping-plus risk scorebased on the histotyping risk score, the PSA level value, and theGleason grade group value; generating a second classification of thepatient as biochemical recurrence (BCR) high-risk or BCR low-risk basedon the histotyping-plus risk score, and a histotyping-plus risk scorethreshold; generating a histotyping-plus BCR prognosis based, at leastin part, on the classification; and displaying the histotyping-plus BCRprognosis.
 16. An apparatus comprising: a processor; a memory configuredto store a digitized image of a region of tissue demonstrating prostatecancer (CaP) pathology; an input/output (I/O) interface; a set ofcircuits; and an interface that connects the processor, the memory, theI/O interface, and the set of circuits, the set of circuits comprising:an image acquisition circuit configured to acquire a digitizedhematoxylin and eosin (H&E) stained image of a region of tissuedemonstrating CaP pathology, where the region of tissue includes a tumorregion, where the region of tissue includes a plurality of gland lumen,where the digitized H&E stained image includes a plurality of pixels, apixel having an intensity, and where the digitized H&E stained image isassociated with a patient; a segmentation circuit configured to generatea set of segmented gland lumen based on the digitized H&E stained image,where the segmentation circuit comprises a deep learning segmentationmodel trained to segment gland lumen represented in a digitized H&Estained image of a region of tissue demonstrating CaP; a post-processingcircuit configured to generate a set of post-processed segmented glandlumen by post-processing the set of segmented gland lumen; aquantitative histomorphometry (QH) circuit configured to: extract a setof QH features based, at least in part, on the set of post-processedsegmented gland lumen; and generate a feature vector based on the set ofQH features; a histotyping risk score circuit configured to: compute aweighted sum of the feature vector; and compute a histotyping risk scorebased on the weighted sum of the feature vector; a classificationcircuit configured to generate a classification of the patient asbiochemical recurrence (BCR) high-risk or BCR low-risk based on thehistotyping risk score and a risk score threshold; a prognostic circuitconfigured to generate a first BCR prognosis based, at least in part, onthe classification, and a second BCR prognosis based on the histotypingrisk score; and a display circuit configured to display at least one ofthe first BCR prognosis, the second BCR prognosis, the classification,the histotyping risk score, the weighted sum of the feature vector, thefeature vector, the set of QH features, the set of post-processedsegmented gland lumen, or the digitized image.
 17. The apparatus ofclaim 16, where the post-processing circuit is configured to: define aset of post-processed segmented gland lumen, where the set ofpost-processed segmented gland lumen includes the members of the set ofsegmented gland lumen; determine if a member of the set ofpost-processed segmented gland lumen includes a non-lumen region; upondetermining that the member of the set of post-processed segmented glandlumen includes a non-lumen region: re-label the non-lumen region aslumen; determine an area of a member of the set of segmented glandlumen; upon determining that the member of the set of segmented glandlumen has an area less than a threshold area: remove the member of theset of segmented gland lumen from the set of post-processed segmentedgland lumen; and determine a boundary of a member of the set ofsegmented gland lumen; upon determining that the boundary of the memberof the set of segmented gland lumen is defined by a white pixel: removethe segmented gland lumen from the set of post-processed segmented glandlumen.
 18. The apparatus of claim 17, the set of circuits furthercomprising a training circuit configured to: select a plurality ofregions of interest (ROIs) from a set of digitized H&E stained images;annotate a gland lumen represented in a member of the plurality of ROIs;generate a first set of resized ROIs by resizing a member of theplurality of ROIs; train a first deep learning model to segment a glandlumen based on the first set of resized ROIs; evaluate the first deeplearning model performance; train a final deep learning model on asecond training set, where the second training set includes the firstset of resized ROIs, and a second, different set of annotated resizedROIs, where the second, different set of annotated resized ROIs isselected based on the evaluated first deep learning model performance;and test the final deep learning model.
 19. The apparatus of claim 16,where the classification circuit is further configured to generate asecond BCR prognosis based, at least in part, on the histotyping riskscore; and where the display circuit is further configured to displaythe second BCR prognosis.
 20. The apparatus of claim 16, the set ofcircuits further comprising: a clinical feature circuit configured to:access a pre-radical prostatectomy (RP) serum prostate specific antigen(PSA) level value associated with the patient; and access a Gleasongrade group value associated with the patient; and a histotyping-pluscircuit configured to: compute a histotyping-plus risk score based onthe histotyping risk score, the PSA level value, and the Gleason gradegroup value; where the classification circuit is further configured togenerate a histotyping-plus classification of the patient as biochemicalrecurrence (BCR) high-risk or BCR low-risk based on the histotyping-plusrisk score and a histotyping-plus risk score threshold; where theprognostic circuit is further configured to generate a histotyping-plusBCR prognosis based, at least in part, on the histotyping-plusclassification; and where the display circuit is further configured todisplay the histotyping-plus BCR prognosis, the histotyping-plusclassification, or the histotyping-plus risk score.
 21. A non-transitorycomputer-readable storage device storing computer-executableinstructions that when executed cause a processor to perform operationsfor generating a biochemical recurrence prognosis associated with apatient demonstrating prostate cancer (CaP) pathology, the operationscomprising: accessing a digitized hematoxylin and eosin (H&E) stainedimage of a region of tissue demonstrating CaP pathology, where theregion of tissue includes an annotated tumor region, where the digitizedH&E stained image includes a plurality of pixels, a pixel having anintensity, and where the digitized H&E stained image is associated witha patient; generating a set of segmented gland lumen by segmenting aplurality of gland lumen represented in the tumor region using a deeplearning segmentation model trained to segment gland lumen on a firsttraining set, and further trained on a second training set thatcomprises the first training set and a second, different set of images;generating a set of post-processed segmented gland lumen by re-labellingmis-labelled areas of a member of the set of segmented gland lumen, andby removing artifacts from the set of segmented gland lumen; extractinga set of quantitative histomorphometry (QH) features based, at least inpart, on the set of post-processed segmented gland lumen, where the setof QH features includes a set of gland lumen features and a set oftexture features, where the set of gland lumen features includes atleast two lumen arrangement features, at least one lumen orientationdisorder feature, and at least ten lumen shape features, and where theset of texture features includes at least two texture features extractedfrom the tumor region; generating a feature vector based on the set ofQH features; computing a histotyping risk score based on a weighted sumof the feature vector; generating a categorical classification of thepatient as biochemical recurrence (BCR) high-risk or BCR low-risk basedon the histotyping risk score and a risk score threshold; generating acontinuous classification of the patient as BCR high-risk or BCRlow-risk based on the histotyping risk score; generating a first BCRprognosis based on the categorical classification; generating a secondBCR prognosis based on continuous classification; and displaying thefirst BCR prognosis or the second BCR prognosis.
 22. A non-transitorycomputer-readable storage device storing computer-executableinstructions that when executed cause a processor to perform operations,the operations comprising: accessing a digitized image of a region oftissue demonstrating prostate cancer (CaP) pathology, where the regionof tissue includes a tumor region, where the digitized image includes aplurality of pixels, a pixel having an intensity, where the digitizedimage is a digitized image of a hematoxylin and eosin (H&E) stainedwhole slide image (WSI) acquired post-radical prostatectomy (RP), andwhere the digitized image is associated with a patient; generating a setof segmented gland lumen by segmenting a plurality of gland lumenrepresented in the tumor region using a deep learning segmentationmodel; generating a set of post-processed segmented gland lumen bypost-processing the set of segmented gland lumen; extracting a set ofquantitative histomorphometry (QH) features from the digitized imagebased, at least in part, on the set of post-processed segmented glandlumen; generating a first feature vector based on the set of QHfeatures; computing a continuous histotyping risk score based on aweighted sum of the first feature vector; accessing a pre-RP serumprostate specific antigen (PSA) level value associated with the patient;accessing a Gleason grade group value associated with the patient;computing a histotyping-plus risk score based on the continuoushistotyping risk score, the PSA level value, and the Gleason grade groupvalue; generating a classification of the patient as biochemicalrecurrence (BCR) high-risk or BCR low-risk based on the histotyping-plusrisk score, and a histotyping-plus risk score threshold; generating aBCR prognosis based, at least in part, on the classification; anddisplaying the BCR prognosis.
 23. The non-transitory computer-readablestorage device of claim 22, where the digitized image includes anannotated tumor region.
 24. The non-transitory computer-readable storagedevice of claim 22, the operations further comprising automaticallyannotating the tumor region.
 25. The non-transitory computer-readablestorage device of claim 22, where generating the post-processed set ofsegmented gland lumen by post-processing the set of segmented glandlumen comprises: defining a set of post-processed segmented gland lumen,where the set of post-processed segmented gland lumen includes themembers of the set of segmented gland lumen; determining if a member ofthe set of post-processed segmented gland lumen includes a non-lumenregion; upon determining that the member of the set of post-processedsegmented gland lumen includes a non-lumen region: re-labelling thenon-lumen region as lumen; determining an area of a member of the set ofsegmented gland lumen; upon determining that the member of the set ofsegmented gland lumen has an area less than a threshold area: removingthe member of the set of segmented gland lumen from the set ofpost-processed segmented gland lumen; and determining a boundary of amember of the set of segmented gland lumen; upon determining that theboundary of the member of the set of segmented gland lumen is defined bya white pixel: removing the segmented gland lumen from the set ofpost-processed segmented gland lumen.
 26. The non-transitorycomputer-readable storage device of claim 25, where the threshold areais 4 μm².
 27. The non-transitory computer-readable storage device ofclaim 22, where the set of QH features includes a mean invariant moment2 feature, a mean Fourier descriptor 4 feature, a standard deviation ofsmoothness feature, a median distance ratio feature, a 5^(th)percentile/95^(th) percentile perimeter ratio feature, a 5^(th)percentile/95^(th) percentile Fourier descriptor 1 feature, a 5^(th)percentile/95^(th) percentile Fourier descriptor 6 feature, a skewnessof edge length sub-graph feature, and a Haralick mean correlationfeature.